Inflation Expectations and Policy Relevance in India

Inflation Expectations and Policy Relevance in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inflation Expectations and Policy Relevance in India Ramgopal Kundurthi, Siva Reddy Kalluru This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7158381/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Post adoption of the Flexible Inflation Targeting by India, the importance of anchoring inflation expectations has been taking centre stage of academic and policy research. The expectations channel is now considered as an important transmission mechanism in itself. This paper explores whether expectation formation in the Indian context can be treated as forward or backward looking with or without adaptive features, using the data from the available surveys. It is observed that the inflation expectation formation of households is predominantly naïve and repetitive and hence does not provide any additional information for policy guidance. Professional Forecasters are also unable to forecast the turning points and are overestimating inflation, thus questioning the policy relevance of such expectations. JEL classifications: E3, E5, E7 Macroeconomics Inflation Expectations Flexible Inflation Targeting Forward Looking Monetary Policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction “But this is a story about outcomes, not expectations.” (Jeremy Rudd, 2021 ) An important link in the mainstream macroeconomic modelling of monetary policy and inflation is the concept of Inflation Expectations and the purported need for anchoring them so as to achieve Central Bank credibility. Inflation expectations, though directly not observable, are supposed to be an underlying decision factor for the homo economicus . After Phelps ( 1967 ) and Friedman ( 1968 ) had emphasised the dynamic nature of the Phillips Curve that denied the leveraged employment Governments hoped to achieve through one time spike in inflation, the long term neutrality of money was accepted and made part of the applications of the New Keynesian Phillips Curve (NKPC) framework. Nominal price/wage rigidities were introduced to give a non-neutrality for monetary policy in the short term. A natural extension of using the NKPC framework for monetary policy had been the adoption of Inflation Targeting either directly or indirectly by most Central Banks. The overall subdued inflation experience of the last four decades in the Developed Countries (DCs) reinforced the hypothesis that the anchored expectations have helped achieving a low and stable inflation environment and a build-up of credibility for these Central Banks. The popularity of this theme has been so much so, that expectations channel is now considered as an important transmission mechanism in itself. Post adoption of the Flexible Inflation Targeting (FIT) by India, the importance of anchoring inflation expectations has been taking centre stage of academic and policy research. In this paper, we attempt to provide an overview of the expectations hypothesis, the empirical studies in the Indian context and an empirical model to test the nature of expectations provided by the surveys of Reserve Bank of India as to whether they can provide meaningful and actionable policy guidance in the Indian context. The rest of the paper is organised as follows. Section 2 discusses the importance, nature, and a critique of inflation expectations hypothesis. Section 3 provides empirical studies related to inflation expectations for India. Section 4 discusses the available surveys of Inflation expectations in India. Section 5 presents empirical model adopted in the paper. Section 6 discusses empirical results and section 7 concludes. 2. Inflation Expectations-Importance, Nature and A critique In the NKPC framework, the inflation expectations are channelled to current inflation through consumption (based on inter-temporal substitution and lower real interest rate considerations) and cost factors (such as wage indexation, wage bargaining, and staggered price setting). Strong Central Bank action (through countervailing interest rates) aided by transparent communication, is expected, over a period of time, to anchor these expectations to the target of the Central Bank. The anticipation of the action in itself would then temper the decision making of the rational economic agents. In that case, the Central Bank is able to reduce the current inflation with lesser output loss than otherwise required (Clarida et al., 1999 ). And, with anchored expectations, the central banks are able to “respond more aggressively to recessionary demand shocks and less aggressively to inflationary supply shocks leading to better dual mandate outcomes” (Bernanke, 2022 ). In the Indian context, too, even before the formal adoption of FIT, it was believed that anchoring expectations is a first step in controlling inflation and minimize output costs (Patra & Ray, 2010 ). The Expert Committee of RBI that recommended the FIT also observed that “Stabilising and anchoring inflation expectations whether they are rational or adaptive is critical for ensuring price stability on an enduring basis” (RBI, 2014). A critical tool in managing expectations is said to be the Central Bank Communication. Post Global Financial Crisis(GFC), transparent communication and forward guidance were considered as an important tool, in addition to the quantitative easing to effect economic outcomes (Coibion et al., 2018 ). As Bernanke ( 2022 ) notes, since monetary policy is 98% talk and 2% action, much of the said better dual mandate outcomes is achieved “…through word…”. However, Coibion et al., ( 2018 ) point out that Central bankers have not attempted to manage the expectations, but only strived to anchor them through speeches, policy statements and press briefings that “have helped reduce financial market volatility”. It seems logical that very short-term changes in actual inflation should not affect the expectations if they are anchored. An acceptable definition is provided by Bernanke that anchoring should mean that the expectations are “relatively insensitive to incoming data.” (Bernanke, 2007 ). This is echoed by others, for instance, short term expectations are considered too volatile and reflect current perceptions (Nielsen, 2022). A slightly different way of looking at anchored expectation is that it is broadly nearer to the inflation target of the central bank (Bonatti et al., 2022 ). Inflation Expectations could be looked at as Forward vs Backward looking. Forward looking expectations are considered proxy for rational and are formed based on all available information projected towards the future and hence supposed to be a good predictor of the future inflation. The backward expectations on the other hand, are based on previous experience and may include a learning or adaptive feature whereby the agents adjust their forecasts based on previous forecast errors; if the past projections are too low, then the expectation is revised upwards and vice versa; This provides for persistence as more realistic expectations are behaviourally slower to form and slower to change. If the adaptive feature is so strong that the last realisation is projected as the next expectation, this could be dubbed as a naïve form of the adaptive expectation (Gerberding, 2001 ). If both the forward and adaptive features are weak, then the expectation would have a simple auto regressive (AR) feature, i.e., future expectation is based on previous expectation with no consideration for rational expectation or adaptive learning. In this paper, this behaviour is called “naively repetitive.” Data on expectations are provided by the surveys conducted, typically among the households and professional market participants. Price expectations of businessmen could be important since they are price-setters; however, such information on those as well as on nominal wage expectations is particularly scarce (Bernanke, 2007 ). In the Developed Countries (DCs), risk premia on market instruments are also considered. Despite its popularity and almost universal acceptance, the idea of expectations as the basis for explaining inflation has been questioned. A sustained low inflation environment enhances the credibility of the Central Bank and generates stable expectations. To, then argue that it is the anchored expectation that leads to subsequent low inflation seems circular in nature. As Goodhart opines that there is no theory of inflation, and what is attempted is a bits and pieces approach; and the resulting bootstrap theory of inflation expectations is a week reed (Goodhart, 2021 ). In a strongly worded critique of expectations theory, Rudd argues that there is no direct evidence on the efficacy of the expected inflation terms, no examination of alternate explanations and no questioning of the assumptions in these models. While there could be auto regressive features functions in the inflation models, “thinking that these lags of inflation are present because they are a proxy for some kind of forecast is more a habit of mind than anything solidly grounded in fact” (Rudd, 2021 ). Prior to the recent episode of global inflation, much of the attention had been on the ability of DC policy rates at or near zero to push up inflation to targeted levels. After the GFC and the recessionary threats faced by the DCs gave raise to doubts on the efficacy of assumptions of absence of money illusion, rational expectations and interest rate transmission, particularly at the zero lower bound (Schnabel, 2020 ). Proponents of the expectations hypothesis acknowledge the issues of causal inference, measurement and ignorance of central bank communication surrounding the same (Bernanke, 2022 ). Others point out that despite the noise and biases seen in the different measures, they provide coherent signals if combined. (Reis in ECB 2022). However, it may not make sense to combine these indicators, “because their properties and their biases are so different” (Bernanke, 2022 ). Yet another issue is whether the inflation expectation surveyed is actually a decision parameter for the households or a mere forecast or a perception. 3. Empirical Evidence - India Using the Inflation Expectations of Household Surveys (IESH) data, Sharma & Bicchal ( 2018 ) conclude that inflation expectations in India are purely backward looking and hence suggesting a low credibility for the central bank. For Consumer Price Index-Industrial Workers (CPI-IW), the expectations are observed to be backward looking with adaptive, static and naïve elements present. And for WPI, the expectation was found to be naïve. Importantly, these “…backward looking formations of expectation do not predict directional change in inflation, raising questions as to their usefulness as proxies for true expectations.” Goyal ( 2016 ) observes that in India there is a “…large share of backward-looking behaviour..….since the aggregate demand channel does not reduce inflation much, too high real interest rates impose unnecessary output costs”. In a similar vein, Goyal & Parab ( 2019a ) observe that the household expectations are adaptive and backward looking; however, the large speed of adjustment, inter-alia bodes well for anchoring expectations. And that there is a gradual movement of households from naïve to adaptive expectations (Goyal & Parab, 2019b ). They opined that communications and expectation channel have more of an impact on inflation expectations than interest rate management. Saakshi & Sahu ( 2019 ) observe that there is considerable heterogeneity across cities and suggest that RBI should enhance its communication with general public. Similar results of backward looking and adaptive expectations were observed by Pattanaik et al., ( 2020 ) that no wage pressures on the prices but high degree of persistence and the dependence of expectations on the food and fuel shocks warrant, in their opinion, a “sustained emphasis of monetary policy on well-anchored inflation expectations.” Chinoy et al ( 2016 ) suggested that there is significant impact of introduction of the Inflation Targeting Regime of India on the forward-looking expectations in addition to the backward-looking expectations. Besides, Dholakia & Kadiyala ( 2018 ) observe that in the recent past, there is a reduction in inflation persistence and behaviour of head-line inflation reverting to the Core (as was assumed and evidenced earlier), signifying low second round effects. Behera & Ranjan ( 2024 ) arrive at similar conclusion that the head-line inflation has been reverting to core rather than the other way. These results have been attributed to the expected reaction to the shift of the regime to inflation targeting and anchoring of expectations. Using text mining techniques, Samanta and Kumari (2021) build a monetary policy Transparency Index that showed an increase post the adoption of FIT. Linking the transition phases of the Index with the one-year ahead expectations from SPF and IESH, it observed anchoring of expectations in the weak form (i.e., not influenced by realised inflation), though for IESH, the anchoring is at a higher level. Singh et al ( 2022 ) observe that the upward bias in the surveys of households is a worldwide phenomenon and not restricted to India alone. However, forecasting inflation through inflation expectations poses challenges in terms of geographical dispersion and supply shocks in different items of consumption basket. Higher expectations also are observed to result in slower term deposit growth (Singh et al., 2022 ). In sharp contrast to the studies as above, Balakrishnan & Parameswaran ( 2019 ) question the theoretical and empirical importance of forward-looking expectations on inflation and the failure of the NKPC framework to provide an adequate description of the developing economy pathology. With this background, in this paper, we look at the data on expectations and attempt to model the same as to whether they represent forward/rational or backward adaptive or naïve form of expectations. 4. Inflation Expectations Surveys – The Indian Scenario In India, RBI has been conducting two surveys since 2007, viz., Inflation Expectations Survey of Households (IESH) and Survey of Professional Forecasters (SPF). The data represents the expectations of various households and the professional forecasters on the expected inflation scenario. In respect of the professionals, the survey generates responses separately for Consumer Price Index (CPI) and Wholesale Price Index (WPI); while in case of households such a distinction is not done. The data on the household (HH) mean expectations of inflation in three periods viz., current, 3 months ahead and 1 year ahead are provided and mapped against CPI in Fig. 1 and WPI in Fig. 2 . The surveys have shifted the benchmark from the CPI-IW to CPI w.e.f. 2014. It may be noted that the weightages of both are very similar till recently and hence the forecasts and the mapping against CPI is in a combined form of CPI-IW till 2014 and CPI thereafter. A comparison of average values of the variables as above is provided in the Table 1 . The data is presented in two periods viz., before and after the adoption of the FIT regime. Table 1 Average Values of HH Inflation Expectations and Realisations : Variable Expectation Expectation Expectation CPI-actual WPI-actual Current 3- mth 1- Yr Average 9.7 10.3 10.6 6.4 4.4 Minimum 7.3 7.9 8.3 2.2 -6.1 Maximum 12.7 12.8 13.5 14.9 16.2 Sep-08-Dec-13 10.6 11.0 11.8 10.4 7.1 Mar-14-Nov-24 9.2 9.9 10.0 5.1 3.5 Source: Authors’ calculations from RBI HH surveys and DBIE From the Figs. 1 and 2 and the Table 1 , it appears that the average expectations have not changed much despite the variations in the CPI and WPI and a drop in the actual inflation levels post 2014. While this seems to suggest a naïve repetitive behaviour of expectation formation of Households, a more formal empirical analysis is done in Section 6 . In respect of the professional forecasters, the surveys include expectations on both WPI and CPI and other variables such as GDP. The data also included forward expectations for the long-term median forecasts for 5 and 10 years, till 2017. The base data is presented in Figs. 3 and 4 . It can be seen that the Professionals’ expectations as can be expected are nearer to the actual realisations, particularly in respect of 3-mth expectations. In addition to these surveys, there are few other data sources that may be considered as proxies for inflation expectations. The SPF data till 2017/18 provided 5 and 10 year ahead expectations of the professionals. RBI has also been conducting consumer confidence surveys that inter-alia, seek to solicit the current perception on the inflation whether it has increased, remained the same and decreased and one year ahead expectation that it will increase, remain the same or decrease. The results are given in percentages of response. The Net response is the difference between the positive feedback (has decreased or will decrease) and the negative feedback (has increased or will increase). Thus, an increase in the net response implies positive feedback and vice versa. The net responses in respect of current perception and the one year ahead expectation are provided below in Fig. 5 . As can be seen, barring a brief period of 2016-17, the negative expectation has remained fairly high (average of -85.4 before 2016 and − 77.6 after 2017), thus the negative expectation has always dominated the positive. The study attempts to model the aforementioned variables as measurement for inflation expectations. 5. Empirical Model and Data In order to model the expectations formally, we consider the mean values of the current, 3 months ahead and 12 months ahead inflation expectations of HHs from the IESH data September 2008 till November 2024, encompassing 82 surveys. In respect of the SPF data, we consider the 3 month ahead and 1 year ahead median forecasts of professionals for the period July 2008 to November 2024. The median data is chosen as long term median expectations are also shown for the professionals till 2017. The CPI and the WPI realisations are mapped to match the survey months for t-1 (previous realisation), t + 3, t + 12 and t + 60. All inflation figures are the year-on-year percentage changes announced on a monthly basis. The matching period mapping is required since the surveys were initially done quarterly and later increased to six surveys in a year from 2014 for SPF and 2017 for IESH. The expectation process is mapped through two sets of equations to ascertain the nature of expectations, viz., forward-looking, backward adaptive, or backward naively repetitive. \(\:{{\pi\:}}_{\text{t}+\text{i}}=c1+c2*{{\text{E}}_{\text{t}}{\pi\:}}_{\text{t}+\text{i}}+{\text{ℇ}}_{\text{t}}\) (1) The forward-looking form Where \(\:{{\pi\:}}_{\text{t}+\text{i}}\) represents the inflation (CPI or WPI) for i=t, t+3, t+12 or t+60 as the case may be, \(\:{\:{\text{E}}_{\text{t}}{\pi\:}}_{\text{t}+\text{i}}\) represents the inflation expectation at time t and \(\:{\text{ℇ}}_{\text{t}}\) is the error term. It is expected that the constant term/bias, c1 to be closer to zero and c2, the expectation coefficient to be closer to 1 if the expectations are forward looking. Auto Regressive (AR) terms for the dependent variable are added where auto-correlation is exhibited. \(\:E{\pi\:}_{t+i}=c1+c2*{\:\pi\:}_{t+i}+c3*{E}_{t-1}{\:\pi\:}_{ti}+c4*{\pi\:}_{t-1}+{ℇ}_{t}\) ..….(2) Combined form This specification combines the forward-looking and backward looking elements. The expectation formation is modelled with the forward term (c2), previous expectation term, or naively repetitive term (c3), and previous realisation/ adaptive term (c4) . An alternate form combining forward and backward expectation has been used by Gerberding ( 2001 ), Sharma and Bichel (2018) and (Pattanaik et al., 2020 ). In such a combination, the c3 and c4 as above are combined as backward looking and further a restriction of unity is imposed on forward plus backward expectations. In this study this formulation has not been shown as the results are broadly similar to the combined form. Further, the modelling of HH expectations against WPI has not been shown in this study, as even a cursory glance at Fig. 2 will indicate a complete disconnect between these variables. All the models are estimated by Ordinary Least Squares(OLS). They are also tested using a dummy term for adoption of FIT regime from 2014 onwards 1 . All the equations are tested with HAC error terms in view of the heteroskedasticity exhibited in few cases. Diagnostic tests of Autocorrelation, Heteroskedasticity, and Parametric stability (Cusum/Cusum square) were satisfied. In few cases Cusum or Cusum square was out of bounds for few period/s before reverting to bounds. In most cases, the specification error (Ramsey Reset) test was also satisfied. 6. Results and Discussion 6.1. Household Expectations The summary model coefficients in respect of the two specifications of HH expectations are provided in Tables 2 and 3 . It can be seen that the estimates for the forward-looking term are not statistically significant for CPI. The AR term is statistically significant in all periods. The joint wald test is rejected for the expectation and the constant. For the CPI as dependent variable the dummy term for FIT regime was statistically significant that shows the level of CPI realisations have come down after 2014. However, in the combined form of estimation, it is seen that in respect of all the three estimations, the forward-looking term has been insignificant and has the negative sign in the 3 mth and 1 year estimates. Thus, clearly the HHs do not exhibit forward looking expectations. The constant/bias term and the AR terms are significant and have strong coefficients. The adaptive coefficients of 0.18 and 0.20 are significant for the 3 month and 1-year expectations. However, the absence of such a significance for the current expectation which is of a much shorter horizon reinforces the notion that the HH expectations are naïvely repetitive. Adding a dummy term, did not change the estimates. In respect of the 1 year estimates, the dummy term is significant at 10% level. However, a significantly larger constant as compared to the estimate without dummy casts doubt on the anchoring connotation of such an estimate. As in the case of the first specification, the lower level of actual realisation could also be an explanation. Thus, the hypothesis that the FIT regime did not change the formation of expectations, cannot be rejected . Table 2 Summary of Model Coefficients-IESH-Forward-Looking Form : Current w/dummy 3 mth w/dummy 1 yr w/dummy Forward-looking Constant 0.66 3.04** 2.58 4.95*** 1.08 4.49** Forward 0.01 0.08 -0.21 -0.16 -0.03 -0.16 Pvs Realisations AR(1) 0.88*** 0.63*** 0.92*** 0.69*** 0.86*** 0.71*** Dummy for FIT -1.89*** -1.79*** -1.38 R^2 0.79 0.82 0.78 0.81 0.78 0.8 DW 1.98 1.82 2.22 2.01 2.1 1.92 Significance level *10%; ** 5%; *** 1% Table 3 Summary of Model Coefficients-IESH- Combined Form : Current w/dummy 3 mth w/dummy 1 yr w/dummy Constant 1.76*** 1.29** 2.36*** 2.14*** 2.65*** 4.34*** Forward 0.07 0.09 -0.04 -0.03 -0.13 -0.19* Pvs Realisations 0.09 0.1 0.18** 0.19** 0.20*** 0.13* Pvs Expectations AR(1) 0.83*** 0.82*** 0.75*** 0.75*** 0.70*** 0.68*** AR(2) -0.26*** -0.26*** -0.19** -0.18** AR(3) 0.15** 0.16** 0.12** 0.13** Dummy for FIT -- 0.26 -- 0.12 -0.85* R^2 0.84 0.84 0.8 0.8 0.74 0.75 DW 2.27 2.26 2.27 2.28 1.63 1.65 Significance level *10%; ** 5%; *** 1% To sum-up, the HH expectations are seen to be backward looking with a strong intercept/bias term. There is dominance of repetitive naïve expectation that depends on the previous expectation with little or no adaptive or forward-looking features in different formulations. This is particularly evident in the fact that (a) all the formulations are giving similar coefficients and bias terms, (b) HHs do not seem to make any distinction to the different periods of expectation and (c) even the expectation of current inflation does not seem to factor in either the CPI or the WPI actual realisations. The FIT regime was dominant in the CPI realisations; however, it was not in the expectations formation. 6.2 Expectations of Professionals It may be noted that most of the participants in the SPF surveys are professional economists from the financial and banking sector and hence can be expected to be well versed with the given monetary policy paradigm and the central bank reaction function. Accordingly, we hypothesise that these expectations would be (a) forward and adaptive in nature and (b) the longer-term expectations would be more stable (and anchored to the FIT). The results of SPF 3 months ahead and 1 year ahead expectations are provided in Tables 4 and 5 Table 4 Results of Model Coefficients-SPF expectations-CPI : SPF-CPI-3mth SPF-CPI-1 yr Forward-looking 3 mth With dummy 1 yr With dummy Constant 0.35 3.25*** 0.996* 4.96*** Forward Expectation 0.31 0.22 -0.06 -0.21** Previous Realisations AR(1) 0.64*** 0.48*** 0.64*** 0.63*** Dummy for FIT regime -1.80*** -2.06** R^2 0.81 0.81 0.75 0.77 DW 1.94 1.94 1.85 2.2 Combined Form Constant 0.72*** 0.45 0.05 1.51** Forward Expectation 0.12** 0.14*** 0.07* 0.01 Previous Realisations 0.51*** 0.52*** 0.11*** 0.06 Previous Expectations AR(1) 0.22* 0.22* 0.47*** 0.45*** AR(4) -- -- 0.32** 0.30** Dummy for FIT regime -- 0.21 -- -0.74* R^2 0.93 0.88 0.89 DW 2.13 1.52 1.46 Significance level *10%; ** 5%; *** 1% Table 5 Summary of Model Coefficients-SPF expectations-WPI : SPF-WPI-3mth SPF-WPI-1yr Forward-looking 3 mth 1 yr With dummy Constant 0.43 0.96* 1.85*** 5.09** Forward Expectation 0.06 0.06 -0.35** -0.77*** Previous Realisations AR(1) 1.23*** 1.22*** 1.18*** 1.08*** AR(2) 0.39*** -0.41*** 0.51*** -0.46*** AR(3) 0.54*** 0.50*** AR(4) 0.31*** -0.24** Dummy for FIT regime -0.61 -1.81 R^2 0.85 0.86 0.90 0.90 DW 1.94 1.95 1.96 1.84 Combined Form Constant 0.92*** 0.97*** 1.12*** 4.12*** Forward Expectation 0.14*** 0.14*** -0.00 -0.06** Previous Realisations WPI(-1) 0.60*** 0.61*** 0.07** 0.10*** WPI(-2) -0.29** -0.29** Previous Expectations AR(1) 0.43*** 0.51*** 0.67*** 0.27* AR(2) -0.17* -0.32* -- -- Dummy for FIT regime -- -0.06 -- -1.57*** R^2 0.94 0.94 0.68 0.75 DW 1.71 1.68 2.11 1.72 Significance level *10%; ** 5%; *** 1% In the forward-looking equation for the CPI, the forward term was not statistically significant at 10% level only for the 3-month expectation. The AR terms and the dummy were similar to that of the HH expectations. In the combined form for the 3-month expectation, the constant, and the adaptive term were dominant. The forward term and the AR term were also statistically significant at 5 and 10% level. The professional forecasters thus exhibited a degree of forward-looking expectations in respect of their 3 month estimates, in addition to updating their expectations with the actual/recent CPI realisations as expected. In respect of 1-year expectation, the results are different, with the dominance of previous expectations, in addition to weaker coefficients of adaptive and forward terms. The coefficient of the dummy was statistically significant, despite a sharply higher constant term, suggestive of the influence of the FIT regime on the longer horizon expectations of the professional forecasters. It could be said that the longer-term expectations of professional forecasters are anchored, while the shorter-term expectations are largely adaptive and forward looking to a limited extent. The WPI realisation was observed to be largely explained by the AR terms up to 2 and 4 periods for the 3 month and 1 year expectations respetively. The forward term(s) had the wrong expected sign (negative) and the bias/constant term was statistically significant for the 1-year expectation. The dummy term was not statistically significant suggesting that the WPI realisations did not have a break in 2014. In the combined form of estimation for the 3-month expectations, it is observed that all the coefficients were statistically significant, though similar to CPI, the adaptive and the constant terms were dominant. The 3-month WPI expectations are therefore seen to be largely backward looking even for professional forecasters. In respect of the 1-year expectations, the results are different. While the constant, adaptive and AR terms were statistically significant, the forward term had the wrong expected sign. The AR term and the constant were dominant. Introduction of dummy term after 2014 changed the adaptive term to be weaker. While the adoption of the FIT regime seemed to make a difference, it is somewhat compromised by a much higher constant. To sum up, the Professional Forecasters, as expected, are seen updating the 3-month expectations with the actual/recent CPI/WPI realisations. They also exhibited a degree of forward-looking expectations in respect of their 3-month estimates. The FIT regime did not influence the formation of 3-month expectations of the forecasters. The FIT regime seemed to have brought a degree of longer-term anchoring to the expectations to the professional forecasters. 6.3 Professional Forecasts and Turning Points In order to gauge the efficacy of the professional forecasts in terms of predicting the turning points, the peaks and troughs in the cycles of CPI previous realisations and the SPF 3 month ahead expectations (that have been mapped as above) are compared using the Business Cycle Dating Program (BBQ) provided by the National Centre for Econometric research (NCER, n.d.). 2 The results are provided in Table 6 . The combined peaks and troughs are represented in Fig. 6 . Table 6 Peaks and Troughs - SPF- 3 mth Expectations vs CPI Actuals : Peaks Troughs Date CPI (t-1) Date Exp-3 mth Date CPI (t-1) Date Exp-3 mth Oct-08 9.77 Apr-09 8.03 Apr-09 6.60 Jan-10 14.97 Apr-10 13.50 Mar-12 7.57 Jan-11 7.40 Apr-13 11.44 Sep-11 8.50 Sep-15 3.70 Mar-12 7.20 Jul-16 5.77 Oct-12 10.40 Jul-17 1.50 Jul-15 4.70 Jan-18 5.21 Jul-16 5.70 Jan-19 2.15 Jul-17 2.50 Jan-20 7.31 Mar-18 5.10 Jul-20 6.16 Mar-19 2.90 Nov-20 7.61 Nov-20 6.60 May-21 4.23 Sep-21 4.70 May-22 7.79 May-22 7.20 May-23 4.70 Jul-24 4.00 Sep-23 6.83 Sep-24 3.65 Dec-24 6.21 Source: Authors’ own calculations based on BBQ algorithm of NCER From the above results, it is observed that in general the peaks and troughs of actual realisations lead the expectation rather than the other way. The average duration of both the contractions and expansions was higher for the expectation as compared to actual (6.5 vs 6 periods in contractions and 4.38 vs 3.3 periods in expansions). The average cumulative movement of contractions is lower for expectation than actual (-13.73% vs -17.63%) and the average cumulative movement for the expansions is higher for the expectation than actual (8.31% vs 6.97%). This demonstrates that the SPF expectations are more persisting and are also averaging higher than actual realisations. As a result, the Professional Forecasters are overestimating inflation and have not been able to forecast turning points. 6.4 Other Proxies SPF-Long Term Expectation As mentioned above, the SPF surveys included long term forecasts for 5 and 10 years between 2008 and 2017 for WPI and 2018 for CPI. As could be seen from Figs. 3 and 4 , the long-term expectations of the professionals seem anchored around the averages of 5.9 and 5.4 for the WPI and 6.9 and 6.0 for the CPI for the 5 and 10 year respectively, prior to the period of the FIT. These have shifted down to 4.2 and 4.3 for WPI and 5.3 and 5.0 for CPI respectively after the FIT. The results for the equations (1) and (4) for long term expectations for the 5 year forecasts for the data period July 2008 to April 2017 (WPI) and till April 2018 (CPI) are provided below. The 10-year expectation is not modelled as there were no sufficient observations for the forward term. Table 7 Summary of Model Coefficients-SPF Long Term expectations : SPF-WPI-5yr SPF-CPI-5 yr Forward-looking Constant 2.87 4.49*** Forward -0.47 -0.38*** Previous Realisation AR(1) 0.94*** 0.58*** Combined Form with dummy with dummy Constant 1.01*** 1.56*** 0.83*** 1.71*** Forward -0.01 -0.004 -0.04*** -0.08*** Previous Realisations 0.05*** 0.03** 0.07*** 0.04** Previous Expectations AR(1) 0.76*** 0.70*** 0.81*** 0.65*** AR(4) 0.12 Dummy for FIT regime -0.38*** -0.38*** Significance level *10%; ** 5%; *** 1% It can be seen that the coefficients for the forward expectations have wrong signs. The previous expectations and the constant are dominant in the formation of expectation, As the expectations are relatively stable, it can be said that these are anchored. The significance of dummy indicates the alignment of the professional economists to the Central Bank mandate on FIT. Consumer Confidence Index (CCI) Similar to the other variables for expectations, we attempt to model the equations (1) and (4) on the CCI data for the period September 2012 to September 2023 as compared to CPI realisations. It is expected that the coefficients linking inflation to the net responses will have a negative sign, as explained therein. The results are given in Table 8 Table 8 Summary of Model Coefficients-CCI -Inflation Expectations : Forward-looking Current 1 yr Constant -1.48* 3.82*** Forward -0.04*** 0.03** Previous Realisations (AR1) 0.73*** 0.71*** R^2 0.74 0.54 DW 1.91 2.24 Combined Form Constant 32.90*** -30.01** Forward -2.46** -0.15 Previous Realisations 0.56 -0.77*** Previous Expectations (AR1) 0.57*** 0.53*** AR(2) 0.20 AR(3) -0.32 R^2 0.60 0.47 DW 1.86 2.00 Without the 3 outliers in 2016 Combined Form Constant 25.23*** 19.59*** Forward -0.64 0.08 Previous Realisations -0.44 -0.38** Previous Expectations (AR1) 0.61*** 0.33** AR(2) 0.39** R^2 0.75 0.70 DW 2.26 2.19 Significance level *10%; ** 5%; *** 1% As can be observed, the findings are similar to the previous ones of IESH, viz., the constant and the previous expectations have dominated the expectation formation while the forward term was weak and/or not statistically significant. For the one-year expectation, the coefficient of previous realisation (adaptive term) was significant but small, thus reinforcing the conclusion that the consumer expectations are naively repetitive with absence of forward-looking features and no major learning attributes. 6.5 Discussion The evidence of the backward looking and naïve, repetitive expectation formation of the households does not augur well for the expectations channel in India. Some researchers have maintained that households have exhibited adaptive expectations. Our results show that such adaptive behaviour is absent for the expectation of even current inflation . The claim of expectations getting anchored as they seem stable without relation to actual realisations, is not appealing since such anchoring seems to be at more than double of the actual realisation. There is more credence to the alternate explanation that there is a complete disconnect between policy pronouncements, actual realisations, and perceptions, despite heightened and transparent communication. To the extent that the SPF expectations are in line with the anchoring concept, it is to be borne in mind that the professionals use similar frameworks as the Central Banks do, thus, “casting doubt on the amount of extra information that they provide” (Bernanke, 2022 ). The Professional Forecasters are also unable to forecast the turning points and are overestimating inflation. To the extent that there are forward looking and/or adaptive features in the professional expectations, it could well be a case of “Only financial market participants and professional forecasters seem to pay much attention to the actions of monetary policy-makers” (Coibion et al., 2018 ). A few of the prior studies argued that since the other channels of transmission are weak, RBI should extract maximum impact through the expectations channel. This would tend to reinforce the circular argument of anchoring vs credibility; more importantly, the underlying causal factors for the inflation are downplayed when importance is given to the hazy concept of expectations. Interestingly, this runs against the prescription that it may be much better for the inflation talk to be off the radar so that there is no buildup of self-fulfilling behaviour (Rudd, 2021 ). Despite all the shortcomings expectations are believed to provide some signals and “the authorities can afford to ignore such signals only if they are very confident that they are pure noise.” (Reis, 2022). However, at some stage if the policy keeps emphasising the anchoring of expectation so much, that it could actually become the overriding target despite the behaviour of actual inflation. Irrationality and bounded rationality The presence of the expectations in the NKPC framework appears to have achieved the consensus on the long-term neutrality of money and the transmission of short-term real effects of shocks to the system. This compromise of sorts, is against the Knightian uncertainty highlighted by Keynes, that leads to herd behaviour and at best a limited interpretation of rationality in forming expectations. Because of such uncertainty, “….the facts of the existing situation enter, in a sense disproportionately, into the formation of our long-term expectations.”(Keynes, 1936 ). The mainstream macroeconomics, despite dubbing the framework as New Keynesian, “..however, seems to have taken a position directly antipodal to that of Keynes” (Nachane, 2018 ). And despite the evidence suggesting that the individuals are “...far from rational and forward looking...,” the canonical models adopted by the Central Banks continue to ignore the issue of money illusion (Schnabel, 2020 ). Structural Nature of Inflation As Bonatti et al ( 2022 ) show, the effect of anchored expectations (that do not track the evolution of actual inflation) and rational expectations (that do) coincide when it is assumed that the shocks to prices and/or output are random in nature. However, when the source of inflation is considered structural in nature (Balakrishnan & Parameshwaran, 2019; and Kundurthi & Kalluru, 2023 ), the inflation control is dependent on whether the policy prescriptions and/or the market forces lead towards resolving such structural issues. If not, the policy outcomes could well be sub-optimal. This is important, as the stated stance of no tolerance to inflation and credibility demands stern demand management with accompanying output sacrifices. 7. Concluding remarks Our results show that the most important aspect of inflation expectations, viz., those of the Households are static and naively repetitive. The professional forecasters are observed to be anchored to the target inflation in the longer term and are adaptive in the shorter term. However, the professionals use similar models as the central bank, and hence there is little incremental policy guidance from the same. The professional forecasters also have been overestimating the inflation and are unable to forecast turning points. As the expectation is observed to be essentially backward looking, there is serious risk of central bank’s policies becoming rear-view driven. If it is believed that expectation drives long term inflation trend, it could lead to a persistence of tight money policies that jeopardize growth. Our empirical results reinforce the proposition that the actual sources of inflation need to be addressed rather than trying to gain an elusive policy credibility chasing a hazy target. *************** Declarations No funding was availed for the paper. The authors report that there are no competing interests to declare References Balakrishnan, P., & Parameswaran, M. (2019, July 13). The Dynamics of Inflation in India . https://web.archive.org/web/20190713053721/http:/cds.edu/wp-content/uploads/2019/06/WP485.pdf Behera, H. K., & Ranjan, A. (2024). Food and Fuel Prices: Second Round Effects on Headline Inflation in India [RBI Bulletin]. https://rbidocs.rbi.org.in/rdocs/Bulletin/PDFs/03AR23042024E16411A151A74ADD9E23F6EC9DD5B168.PDF Bernanke, B. (2007, July 10). Inflation Expectations and Inflation Forecasting. Speech . Monetary Economics Workshop, NBER Summer Institute, Cambridge, Massachusetts. https://www.federalreserve.gov/newsevents/speech/bernanke20070710a.htm Bernanke, B. (2022, May 19). Inflation Expectations and Monetary Policy. Keynote Address . Inflation Expectations: Determinants and Consequences, Spring 2022. https://www.youtube.com/watch?v=mV9sPolnFoI&t=2s Bonatti, L., Fracasso, A., & Tamborini, R. (2022). What to expect from inflation expectations: Theory, empirics and policy issues. Publication for the Committee on Economic and Monetary Affairs, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament, Luxembourg, 2022 . Chinoy, S., Kumar, P., & Mishra, P. (2016). What is Responsible for India’s Sharp Disinflation? (WP/16/166) [IMF Working Papers]. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/What-is-Responsible-for-Indias-Sharp-Disinflation-44175 Clarida, R., Galí, J., & Gertler, M. (1999). The Science of Monetary Policy: A New Keynesian Perspective. Journal of Economic Literature , 37 (4), 1661–1707. Coibion, O., Gorodnichenko, Y., Kumar, S., & Pedemonte, M. (2018). Inflation Expectations as a Policy Tool? (Working Paper 24788). National Bureau of Economic Research. https://doi.org/10.3386/w24788 Dholakia, R. H., & Kadiyala, V. S. (2018). Changing Dynamics of Inflation in India. Economic and Political Weekly , 53 (9). https://www.epw.in/journal/2018/9/special-articles/changing-dynamics-inflation-india.html Friedman, M. (1968). The Role of Monetary Policy. The American Economic Review , LVIII (1). Gerberding, C. (2001). The Information Content of Survey Data on Expected Price Developments for Monetary Policy . https://doi.org/10.2139/ssrn.2785125 Goodhart, C. (2021, September 28). The Future of Inflation. Beyond the Pandemic: The Future of Monetary Policy . ECB Forum on Central Banking, 2021. https://www.ecb.europa.eu/pub/pdf/sintra/ecb.forum_central_banking.202112~3d9f018812.en.pdf Goyal, A. (2016). Unconventional monetary policy in emerging markets. Macroeconomics and Finance in Emerging Market Economies , 9 (2), 101–108. https://doi.org/10.1080/17520843.2016.1180835 Goyal, A., & Parab, P. (2019a). Modeling heterogeneity and rationality of inflation expectations across Indian households. Indira Gandhi Institute of Development Research, Mumbai Working Papers , Article 2019–02. https://ideas.repec.org//p/ind/igiwpp/2019-02.html Goyal, A., & Parab, P. (2019b). Inflation Convergence and Anchoring of Expectations in India . Indira Gandhi Institute of Development Research. http://www.igidr.ac.in/working-paper-inflation-convergence-anchoring-expectations-india/ Keynes, J. M. (1936). General Theory Of Employment , Interest And Money (2016th–04 ed.). Atlantic Publishers & Dist. Kundurthi, R., & Kalluru, S. R. (2023). Inflation Dynamics in India—A Structural View (SSRN Scholarly Paper 4604642). https://doi.org/10.2139/ssrn.4604642 Nachane, D. M. (2018). Critique of the New Consensus Macroeconomics and Implications for India . Springer. https://econpapers.repec.org/bookchap/sprisbuec/978-81-322-3920-8.htm NCER. (n.d.). NCER - Data and code . NCER. Retrieved May 14, 2024, from http://ncer.edu.au/resources/data-and-code.php Patra, M. D., & Ray, P. (2010). Inflation Expectations and Monetary Policy in India: An Empirical Exploration (IMF Working Papers). https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Inflation-Expectations-and-Monetary-Policy-in-India-An-Empirical-Exploration-23719 Pattanaik, S., Muduli, S., & Ray, S. (2020). Inflation expectations of households: Do they influence wage-price dynamics in India? Macroeconomics and Finance in Emerging Market Economies , 13 (3), 244–263. https://doi.org/10.1080/17520843.2020.1720264 Phelps, E. S. (1967). Phillips Curves, Expectations of Inflation and Optimal Unemployment over Time. Economica , 34 (135), 254–281. https://doi.org/10.2307/2552025 Rudd, J. B. (2021). Why Do We Think That Inflation Expectations Matter for Inflation? (And Should We?) (Finance and Economics Discussion Series (FEDS)) [Working Paper]. Board of Governors of Feederal Reserve System. https://www.federalreserve.gov/econres/feds/why-do-we-think-that-inflation-expectations-matter-for-Inflation-and-should-we.htm Saakshi, & Sahu, S. (2019). An analysis of heterogeneity in inflation expectations across cities in India. Journal of Economic Studies , 46 (5), 1116–1136. https://doi.org/10.1108/JES-08-2018-0297 Schnabel, I. (2020, November 24). COVID-19 and monetary policy: Reinforcing prevailing challenges. Speech . The Bank of Finland Monetary Policy webinar: New Challenges to Monetary Policy Strategies, Frankfurt. https://www.ecb.europa.eu/press/key/date/2020/html/ecb.sp201124~bcaebee7c0.en.html Sharma, N. K., & Bicchal, M. (2018). The properties of inflation expectations: Evidence for India. EconomiA , 19 (1), 74–89. https://doi.org/10.1016/j.econ.2017.12.002 Singh, D. P., Mishra, A., & Shaw, P. (2022). Taking Cognisance of Households’ Inflation Expectations in India. RBI -DEAP Research . https://www.rbi.org.in/Scripts/PublicationsView.aspx?id=20991 Footnotes Though formal legislation was done in 2016, the policy documents suggest adoption from 2014 onwards, also evidenced by the shifting of object of reference of surveys from CPI-IW to CPI. The algorithm uses parameters such as symmetric window (period on either side of the peak/trough), phase (number of periods of expansion/contraction), cycle (number of periods from peak to peak) and a threshold percent change to qualify for directional turn. The study used a minimum phase of 2 periods, minimum cycle of 3 periods, symmetric window of 1 period and a threshold level of 15%. Additional Declarations The authors declare no competing interests. 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Realisations\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7158381/v1/01c5b4a8d89afee9fdeef97e.png"},{"id":87201051,"identity":"27dcb4d2-4310-44b6-8417-6af4c47d1187","added_by":"auto","created_at":"2025-07-21 13:18:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56171,"visible":true,"origin":"","legend":"\u003cp\u003eHH Inflation Expectations and WPI Realisations\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7158381/v1/eaf1316c9061316ee53249c9.png"},{"id":87201054,"identity":"b1e6f076-5186-471a-9498-5e8862dc7597","added_by":"auto","created_at":"2025-07-21 13:18:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59676,"visible":true,"origin":"","legend":"\u003cp\u003eSPF Inflation Expectations – CPI\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7158381/v1/6b020618fc72ea6c6c681df2.png"},{"id":87202104,"identity":"ecb2aa63-6b3a-4022-bdf6-4f9e9c619bdf","added_by":"auto","created_at":"2025-07-21 13:26:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58285,"visible":true,"origin":"","legend":"\u003cp\u003eSPF Inflation Expectations – WPI\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7158381/v1/657de6538a90600868eb83ac.png"},{"id":87201061,"identity":"706ecfb1-d117-4c4c-81a4-7736caa747d8","added_by":"auto","created_at":"2025-07-21 13:18:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46834,"visible":true,"origin":"","legend":"\u003cp\u003eCCI Inflation Expectations\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7158381/v1/d5449ca4ccfe6a4eac098ca7.png"},{"id":87201057,"identity":"a73341e5-1efc-4063-94c8-9fc5ceae2b2b","added_by":"auto","created_at":"2025-07-21 13:18:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37105,"visible":true,"origin":"","legend":"\u003cp\u003ePeaks/Troughs – SPF 3 mth expectations and CPI actuals\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7158381/v1/d202cb369d1fdab80ac855c8.png"},{"id":87202392,"identity":"8757f544-e1ce-480f-a400-80883388e57e","added_by":"auto","created_at":"2025-07-21 13:34:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1293484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7158381/v1/971e6ece-7861-44a3-b5bc-110f5cd157c7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInflation Expectations and Policy Relevance in India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cem\u003e\u0026ldquo;But this is a story about outcomes, not expectations.\u0026rdquo; (Jeremy\u003c/em\u003e Rudd, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAn important link in the mainstream macroeconomic modelling of monetary policy and inflation is the concept of Inflation Expectations and the purported need for anchoring them so as to achieve Central Bank credibility. Inflation expectations, though directly not observable, are supposed to be an underlying decision factor for the \u003cem\u003ehomo economicus\u003c/em\u003e. After Phelps (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) and Friedman (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1968\u003c/span\u003e) had emphasised the dynamic nature of the Phillips Curve that denied the leveraged employment Governments hoped to achieve through one time spike in inflation, the long term neutrality of money was accepted and made part of the applications of the New Keynesian Phillips Curve (NKPC) framework. Nominal price/wage rigidities were introduced to give a non-neutrality for monetary policy in the short term. A natural extension of using the NKPC framework for monetary policy had been the adoption of Inflation Targeting either directly or indirectly by most Central Banks. The overall subdued inflation experience of the last four decades in the Developed Countries (DCs) reinforced the hypothesis that the anchored expectations have helped achieving a low and stable inflation environment and a build-up of credibility for these Central Banks. The popularity of this theme has been so much so, that expectations channel is now considered as an important transmission mechanism in itself. Post adoption of the Flexible Inflation Targeting (FIT) by India, the importance of anchoring inflation expectations has been taking centre stage of academic and policy research. In this paper, we attempt to provide an overview of the expectations hypothesis, the empirical studies in the Indian context and an empirical model to test the nature of expectations provided by the surveys of Reserve Bank of India as to whether they can provide meaningful and actionable policy guidance in the Indian context. The rest of the paper is organised as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e discusses the importance, nature, and a critique of inflation expectations hypothesis. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides empirical studies related to inflation expectations for India. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the available surveys of Inflation expectations in India. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents empirical model adopted in the paper. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e6\u003c/span\u003e discusses empirical results and section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e7\u003c/span\u003e concludes.\u003c/p\u003e"},{"header":"2. Inflation Expectations-Importance, Nature and A critique","content":"\u003cp\u003eIn the NKPC framework, the inflation expectations are channelled to current inflation through consumption (based on inter-temporal substitution and lower real interest rate considerations) and cost factors (such as wage indexation, wage bargaining, and staggered price setting). Strong Central Bank action (through countervailing interest rates) aided by transparent communication, is expected, over a period of time, to anchor these expectations to the target of the Central Bank. The anticipation of the action in itself would then temper the decision making of the rational economic agents. In that case, the Central Bank is able to reduce the current inflation with lesser output loss than otherwise required (Clarida et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). And, with anchored expectations, the central banks are able to \u0026ldquo;respond more aggressively to recessionary demand shocks and less aggressively to inflationary supply shocks leading to better dual mandate outcomes\u0026rdquo; (Bernanke, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the Indian context, too, even before the formal adoption of FIT, it was believed that anchoring expectations is a first step in controlling inflation and minimize output costs (Patra \u0026amp; Ray, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Expert Committee of RBI that recommended the FIT also observed that \u0026ldquo;Stabilising and anchoring inflation expectations whether they are rational or adaptive is critical for ensuring price stability on an enduring basis\u0026rdquo; (RBI, 2014).\u003c/p\u003e\u003cp\u003eA critical tool in managing expectations is said to be the Central Bank Communication. Post Global Financial Crisis(GFC), transparent communication and forward guidance were considered as an important tool, in addition to the quantitative easing to effect economic outcomes (Coibion et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As Bernanke (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) notes, since monetary policy is 98% talk and 2% action, much of the said better dual mandate outcomes is achieved \u0026ldquo;\u0026hellip;through word\u0026hellip;\u0026rdquo;. However, Coibion et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) point out that Central bankers have not attempted to manage the expectations, but only strived to anchor them through speeches, policy statements and press briefings that \u0026ldquo;have helped reduce financial market volatility\u0026rdquo;.\u003c/p\u003e\u003cp\u003eIt seems logical that very short-term changes in actual inflation should not affect the expectations if they are anchored. An acceptable definition is provided by Bernanke that anchoring should mean that the expectations are \u0026ldquo;relatively insensitive to incoming data.\u0026rdquo; (Bernanke, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This is echoed by others, for instance, short term expectations are considered too volatile and reflect current perceptions (Nielsen, 2022). A slightly different way of looking at anchored expectation is that it is broadly nearer to the inflation target of the central bank (Bonatti et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Inflation Expectations could be looked at as Forward vs Backward looking. Forward looking expectations are considered proxy for rational and are formed based on all available information projected towards the future and hence supposed to be a good predictor of the future inflation. The backward expectations on the other hand, are based on previous experience and may include a learning or adaptive feature whereby the agents adjust their forecasts based on previous forecast errors; if the past projections are too low, then the expectation is revised upwards and vice versa; This provides for persistence as more realistic expectations are behaviourally slower to form and slower to change. If the adaptive feature is so strong that the last realisation is projected as the next expectation, this could be dubbed as a na\u0026iuml;ve form of the adaptive expectation (Gerberding, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). If both the forward and adaptive features are weak, then the expectation would have a simple auto regressive (AR) feature, i.e., future expectation is based on previous expectation with no consideration for rational expectation or adaptive learning. \u003cem\u003eIn this paper, this behaviour is called \u0026ldquo;naively repetitive.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003eData on expectations are provided by the surveys conducted, typically among the households and professional market participants. Price expectations of businessmen could be important since they are price-setters; however, such information on those as well as on nominal wage expectations is particularly scarce (Bernanke, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the Developed Countries (DCs), risk premia on market instruments are also considered.\u003c/p\u003e\u003cp\u003eDespite its popularity and almost universal acceptance, the idea of expectations as the basis for explaining inflation has been questioned. A sustained low inflation environment enhances the credibility of the Central Bank and generates stable expectations. To, then argue that it is the anchored expectation that leads to subsequent low inflation seems circular in nature. As Goodhart opines that there is no theory of inflation, and what is attempted is a bits and pieces approach; and the resulting bootstrap theory of inflation expectations is a week reed (Goodhart, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In a strongly worded critique of expectations theory, Rudd argues that there is no direct evidence on the efficacy of the expected inflation terms, no examination of alternate explanations and no questioning of the assumptions in these models. While there could be auto regressive features functions in the inflation models, \u0026ldquo;thinking that these lags of inflation are present because they are a proxy for some kind of forecast is more a habit of mind than anything solidly grounded in fact\u0026rdquo; (Rudd, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrior to the recent episode of global inflation, much of the attention had been on the ability of DC policy rates at or near zero to push up inflation to targeted levels. After the GFC and the recessionary threats faced by the DCs gave raise to doubts on the efficacy of assumptions of absence of money illusion, rational expectations and interest rate transmission, particularly at the zero lower bound (Schnabel, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Proponents of the expectations hypothesis acknowledge the issues of causal inference, measurement and ignorance of central bank communication surrounding the same (Bernanke, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Others point out that despite the noise and biases seen in the different measures, they provide coherent signals if combined. (Reis in ECB 2022). However, it may not make sense to combine these indicators, \u0026ldquo;because their properties and their biases are so different\u0026rdquo; (Bernanke, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet another issue is whether the inflation expectation surveyed is actually a decision parameter for the households or a mere forecast or a perception.\u003c/p\u003e"},{"header":"3. Empirical Evidence - India","content":"\u003cp\u003eUsing the Inflation Expectations of Household Surveys (IESH) data, Sharma \u0026amp; Bicchal (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conclude that inflation expectations in India are purely backward looking and hence suggesting a low credibility for the central bank. For Consumer Price Index-Industrial Workers (CPI-IW), the expectations are observed to be backward looking with adaptive, static and na\u0026iuml;ve elements present. And for WPI, the expectation was found to be na\u0026iuml;ve. Importantly, these \u0026ldquo;\u0026hellip;backward looking formations of expectation do not predict directional change in inflation, raising questions as to their usefulness as proxies for true expectations.\u0026rdquo; Goyal (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) observes that in India there is a \u0026ldquo;\u0026hellip;large share of backward-looking behaviour..\u0026hellip;.since the aggregate demand channel does not reduce inflation much, too high real interest rates impose unnecessary output costs\u0026rdquo;. In a similar vein, Goyal \u0026amp; Parab (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) observe that the household expectations are adaptive and backward looking; however, the large speed of adjustment, inter-alia bodes well for anchoring expectations. And that there is a gradual movement of households from na\u0026iuml;ve to adaptive expectations (Goyal \u0026amp; Parab, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). They opined that communications and expectation channel have more of an impact on inflation expectations than interest rate management. Saakshi \u0026amp; Sahu (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observe that there is considerable heterogeneity across cities and suggest that RBI should enhance its communication with general public. Similar results of backward looking and adaptive expectations were observed by Pattanaik et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) that no wage pressures on the prices but high degree of persistence and the dependence of expectations on the food and fuel shocks warrant, in their opinion, a \u0026ldquo;sustained emphasis of monetary policy on well-anchored inflation expectations.\u0026rdquo;\u003c/p\u003e\u003cp\u003eChinoy et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) suggested that there is significant impact of introduction of the Inflation Targeting Regime of India on the forward-looking expectations in addition to the backward-looking expectations. Besides, Dholakia \u0026amp; Kadiyala (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) observe that in the recent past, there is a reduction in inflation persistence and behaviour of head-line inflation reverting to the Core (as was assumed and evidenced earlier), signifying low second round effects. Behera \u0026amp; Ranjan (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) arrive at similar conclusion that the head-line inflation has been reverting to core rather than the other way. These results have been attributed to the expected reaction to the shift of the regime to inflation targeting and anchoring of expectations. Using text mining techniques, Samanta and Kumari (2021) build a monetary policy Transparency Index that showed an increase post the adoption of FIT. Linking the transition phases of the Index with the one-year ahead expectations from SPF and IESH, it observed anchoring of expectations in the weak form (i.e., not influenced by realised inflation), though for IESH, the anchoring is at a higher level. Singh et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) observe that the upward bias in the surveys of households is a worldwide phenomenon and not restricted to India alone. However, forecasting inflation through inflation expectations poses challenges in terms of geographical dispersion and supply shocks in different items of consumption basket. Higher expectations also are observed to result in slower term deposit growth (Singh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn sharp contrast to the studies as above, Balakrishnan \u0026amp; Parameswaran (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) question the theoretical and empirical importance of forward-looking expectations on inflation and the failure of the NKPC framework to provide an adequate description of the developing economy pathology. With this background, in this paper, we look at the data on expectations and attempt to model the same as to whether they represent forward/rational or backward adaptive or na\u0026iuml;ve form of expectations.\u003c/p\u003e"},{"header":"4. Inflation Expectations Surveys – The Indian Scenario","content":"\u003cp\u003eIn India, RBI has been conducting two surveys since 2007, viz., Inflation Expectations Survey of Households (IESH) and Survey of Professional Forecasters (SPF). The data represents the expectations of various households and the professional forecasters on the expected inflation scenario. In respect of the professionals, the survey generates responses separately for Consumer Price Index (CPI) and Wholesale Price Index (WPI); while in case of households such a distinction is not done. The data on the household (HH) mean expectations of inflation in three periods viz., current, 3 months ahead and 1 year ahead are provided and mapped against CPI in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and WPI in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The surveys have shifted the benchmark from the CPI-IW to CPI w.e.f. 2014. It may be noted that the weightages of both are very similar till recently and hence the forecasts and the mapping against CPI is in a combined form of CPI-IW till 2014 and CPI thereafter.\u003c/p\u003e\n\u003cp\u003eA comparison of average values of the variables as above is provided in the Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The data is presented in two periods viz., before and after the adoption of the FIT regime.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAverage Values of HH Inflation Expectations and Realisations\u003c/span\u003e:\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpectation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpectation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpectation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCPI-actual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWPI-actual\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3- mth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1- Yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSep-08-Dec-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMar-14-Nov-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; calculations from RBI HH surveys and DBIE\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom the Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and the Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, it appears that the average expectations have not changed much despite the variations in the CPI and WPI and a drop in the actual inflation levels post 2014. While this seems to suggest a na\u0026iuml;ve repetitive behaviour of expectation formation of Households, a more formal empirical analysis is done in Section \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. In respect of the professional forecasters, the surveys include expectations on both WPI and CPI and other variables such as GDP. The data also included forward expectations for the long-term median forecasts for 5 and 10 years, till 2017. The base data is presented in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. It can be seen that the Professionals\u0026rsquo; expectations as can be expected are nearer to the actual realisations, particularly in respect of 3-mth expectations.\u003c/p\u003e\n\u003cp\u003eIn addition to these surveys, there are few other data sources that may be considered as proxies for inflation expectations. The SPF data till 2017/18 provided 5 and 10 year ahead expectations of the professionals. RBI has also been conducting consumer confidence surveys that inter-alia, seek to solicit the current perception on the inflation whether it has increased, remained the same and decreased and one year ahead expectation that it will increase, remain the same or decrease. The results are given in percentages of response. The Net response is the difference between the positive feedback (has decreased or will decrease) and the negative feedback (has increased or will increase). Thus, an increase in the net response implies positive feedback and vice versa. The net responses in respect of current perception and the one year ahead expectation are provided below in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eAs can be seen, barring a brief period of 2016-17, the negative expectation has remained fairly high (average of -85.4 before 2016 and \u0026minus;\u0026thinsp;77.6 after 2017), thus the negative expectation has always dominated the positive. The study attempts to model the aforementioned variables as measurement for inflation expectations.\u003c/p\u003e"},{"header":"5. Empirical Model and Data","content":"\u003cp\u003eIn order to model the expectations formally, we consider the mean values of the current, 3 months ahead and 12 months ahead inflation expectations of HHs from the IESH data September 2008 till November 2024, encompassing 82 surveys. In respect of the SPF data, we consider the 3 month ahead and 1 year ahead median forecasts of professionals for the period July 2008 to November 2024. The median data is chosen as long term median expectations are also shown for the professionals till 2017. The CPI and the WPI realisations are mapped to match the survey months for t-1 (previous realisation), t\u0026thinsp;+\u0026thinsp;3, t\u0026thinsp;+\u0026thinsp;12 and t\u0026thinsp;+\u0026thinsp;60. All inflation figures are the year-on-year percentage changes announced on a monthly basis. The matching period mapping is required since the surveys were initially done quarterly and later increased to six surveys in a year from 2014 for SPF and 2017 for IESH.\u003c/p\u003e\u003cp\u003eThe expectation process is mapped through two sets of equations to ascertain the nature of expectations, viz., forward-looking, backward adaptive, or backward naively repetitive.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\pi\\:}}_{\\text{t}+\\text{i}}=c1+c2*{{\\text{E}}_{\\text{t}}{\\pi\\:}}_{\\text{t}+\\text{i}}+{\\text{ℇ}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e (1) \u003cem\u003eThe forward-looking form\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\pi\\:}}_{\\text{t}+\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the inflation (CPI or WPI) for i=t, t+3, t+12 or t+60 as the case may be, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:{\\text{E}}_{\\text{t}}{\\pi\\:}}_{\\text{t}+\\text{i}}\\)\u003c/span\u003e\u003c/span\u003erepresents the inflation expectation at time t and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{ℇ}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the error term. It is expected that the constant term/bias, c1 to be closer to zero and c2, the expectation coefficient to be closer to 1 if the expectations are forward looking. Auto Regressive (AR) terms for the dependent variable are added where auto-correlation is exhibited.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E{\\pi\\:}_{t+i}=c1+c2*{\\:\\pi\\:}_{t+i}+c3*{E}_{t-1}{\\:\\pi\\:}_{ti}+c4*{\\pi\\:}_{t-1}+{ℇ}_{t}\\)\u003c/span\u003e\u003c/span\u003e ..\u0026hellip;.(2) \u003cem\u003eCombined form\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis specification combines the forward-looking and backward looking elements. The expectation formation is modelled with the \u003cem\u003eforward term (c2), previous expectation term, or naively repetitive term (c3), and previous realisation/ adaptive term (c4)\u003c/em\u003e. An alternate form combining forward and backward expectation has been used by Gerberding (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), Sharma and Bichel (2018) and (Pattanaik et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In such a combination, the c3 and c4 as above are combined as backward looking and further a restriction of unity is imposed on forward plus backward expectations. In this study this formulation has not been shown as the results are broadly similar to the combined form. Further, the modelling of HH expectations against WPI has not been shown in this study, as even a cursory glance at Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e will indicate a complete disconnect between these variables.\u003c/p\u003e\u003cp\u003eAll the models are estimated by Ordinary Least Squares(OLS). They are also tested using a dummy term for adoption of FIT regime from 2014 onwards\u003csup\u003e1\u003c/sup\u003e. All the equations are tested with HAC error terms in view of the heteroskedasticity exhibited in few cases. Diagnostic tests of Autocorrelation, Heteroskedasticity, and Parametric stability (Cusum/Cusum square) were satisfied. In few cases Cusum or Cusum square was out of bounds for few period/s before reverting to bounds. In most cases, the specification error (Ramsey Reset) test was also satisfied.\u003c/p\u003e"},{"header":"6. Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Household Expectations\u003c/h2\u003e\u003cp\u003eThe summary model coefficients in respect of the two specifications of HH expectations are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It can be seen that the estimates for the forward-looking term are not statistically significant for CPI. The AR term is statistically significant in all periods. The joint wald test is rejected for the expectation and the constant. For the CPI as dependent variable the dummy term for FIT regime was statistically significant that shows the level of CPI realisations have come down after 2014. However, in the combined form of estimation, it is seen that in respect of all the three estimations, the forward-looking term has been insignificant and has the negative sign in the 3 mth and 1\u0026nbsp;year estimates. Thus, clearly the HHs do not exhibit forward looking expectations. The constant/bias term and the AR terms are significant and have strong coefficients. The adaptive coefficients of 0.18 and 0.20 are significant for the 3 month and 1-year expectations. \u003cem\u003eHowever, the absence of such a significance for the current expectation which is of a much shorter horizon reinforces the notion that the HH expectations are na\u0026iuml;vely repetitive.\u003c/em\u003e Adding a dummy term, did not change the estimates. In respect of the 1\u0026nbsp;year estimates, the dummy term is significant at 10% level. However, a significantly larger constant as compared to the estimate without dummy casts doubt on the anchoring connotation of such an estimate. As in the case of the first specification, the lower level of actual realisation could also be an explanation. \u003cem\u003eThus, the hypothesis that the FIT regime did not change the formation of expectations, cannot be rejected\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSummary of Model Coefficients-IESH-Forward-Looking Form\u003c/span\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ew/dummy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 mth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ew/dummy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 yr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ew/dummy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eForward-looking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.04**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.95***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.49**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePvs Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.63***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.69***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.71***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDummy for FIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-1.89***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.79***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003e*10%; ** 5%; *** 1%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSummary of Model Coefficients-IESH- Combined Form\u003c/span\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ew/dummy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 mth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ew/dummy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 yr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ew/dummy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.76***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.29**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.36***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.14***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.65***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.34***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.19*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePvs Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.20***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.13*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePvs Expectations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.82***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.75***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.70***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.68***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.26***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.26***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.19**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.18**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.16**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDummy for FIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.85*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003e*10%; ** 5%; *** 1%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo sum-up, the HH expectations are seen to be backward looking with a strong intercept/bias term. There is dominance of repetitive na\u0026iuml;ve expectation that depends on the previous expectation with little or no adaptive or forward-looking features in different formulations. This is particularly evident in the fact that (a) all the formulations are giving similar coefficients and bias terms, (b) HHs do not seem to make any distinction to the different periods of expectation and (c) even the expectation of current inflation does not seem to factor in either the CPI or the WPI actual realisations. The FIT regime was dominant in the CPI realisations; however, it was not in the expectations formation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Expectations of Professionals\u003c/h2\u003e\u003cp\u003eIt may be noted that most of the participants in the SPF surveys are professional economists from the financial and banking sector and hence can be expected to be well versed with the given monetary policy paradigm and the central bank reaction function. Accordingly, we hypothesise that these expectations would be (a) forward and adaptive in nature and (b) the longer-term expectations would be more stable (and anchored to the FIT). The results of SPF 3 months ahead and 1 year ahead expectations are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eResults of Model Coefficients-SPF expectations-CPI\u003c/span\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSPF-CPI-3mth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eSPF-CPI-1 yr\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eForward-looking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 mth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWith dummy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWith dummy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.25***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.996*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.96***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward Expectation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.21**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDummy for FIT regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.80***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.06**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Form\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.51**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward Expectation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Expectations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.22*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.47***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.45***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.30**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDummy for FIT regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.74*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003e*10%; ** 5%; *** 1%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSummary of Model Coefficients-SPF expectations-WPI\u003c/span\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eSPF-WPI-3mth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eSPF-WPI-1yr\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eForward-looking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 mth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eWith dummy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.96*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.85***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.09**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward Expectation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.35**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.77***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.22***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.18***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.08***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.41***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.51***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.46***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.54***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.50***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.31***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.24**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDummy for FIT regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Form\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.97***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.12***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.12***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward Expectation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.14***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.06**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWPI(-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.61***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWPI(-2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.29**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.29**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Expectations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.51***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.67***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.27*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.17*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.32*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDummy for FIT regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.57***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003e*10%; ** 5%; *** 1%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the forward-looking equation for the CPI, the forward term was not statistically significant at 10% level only for the 3-month expectation. The AR terms and the dummy were similar to that of the HH expectations. In the combined form for the 3-month expectation, the constant, and the adaptive term were dominant. The forward term and the AR term were also statistically significant at 5 and 10% level. The professional forecasters thus exhibited a degree of forward-looking expectations in respect of their 3 month estimates, in addition to updating their expectations with the actual/recent CPI realisations as expected. In respect of 1-year expectation, the results are different, with the dominance of previous expectations, in addition to weaker coefficients of adaptive and forward terms. The coefficient of the dummy was statistically significant, despite a sharply higher constant term, suggestive of the influence of the FIT regime on the longer horizon expectations of the professional forecasters. It could be said that the longer-term expectations of professional forecasters are anchored, while the shorter-term expectations are largely adaptive and forward looking to a limited extent.\u003c/p\u003e\u003cp\u003eThe WPI realisation was observed to be largely explained by the AR terms up to 2 and 4 periods for the 3 month and 1 year expectations respetively. The forward term(s) had the wrong expected sign (negative) and the bias/constant term was statistically significant for the 1-year expectation. The dummy term was not statistically significant suggesting that the WPI realisations did not have a break in 2014. In the combined form of estimation for the 3-month expectations, it is observed that all the coefficients were statistically significant, though similar to CPI, the adaptive and the constant terms were dominant. The 3-month WPI expectations are therefore seen to be largely backward looking even for professional forecasters. In respect of the 1-year expectations, the results are different. While the constant, adaptive and AR terms were statistically significant, the forward term had the wrong expected sign. The AR term and the constant were dominant. Introduction of dummy term after 2014 changed the adaptive term to be weaker. While the adoption of the FIT regime seemed to make a difference, it is somewhat compromised by a much higher constant.\u003c/p\u003e\u003cp\u003eTo sum up, the Professional Forecasters, as expected, are seen updating the 3-month expectations with the actual/recent CPI/WPI realisations. They also exhibited a degree of forward-looking expectations in respect of their 3-month estimates. The FIT regime did not influence the formation of 3-month expectations of the forecasters. The FIT regime seemed to have brought a degree of longer-term anchoring to the expectations to the professional forecasters.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Professional Forecasts and Turning Points\u003c/h2\u003e\u003cp\u003eIn order to gauge the efficacy of the professional forecasts in terms of predicting the turning points, the peaks and troughs in the cycles of CPI previous realisations and the SPF 3 month ahead expectations (that have been mapped as above) are compared using the Business Cycle Dating Program (BBQ) provided by the National Centre for Econometric research (NCER, n.d.). \u003csup\u003e2\u003c/sup\u003e The results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The combined peaks and troughs are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePeaks and Troughs - SPF- 3 mth Expectations vs CPI Actuals\u003c/span\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeaks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTroughs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCPI (t-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExp-3 mth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCPI (t-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eExp-3 mth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOct-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eApr-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eApr-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJan-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eApr-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMar-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJan-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApr-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSep-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSep-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMar-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJul-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOct-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJul-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJul-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJan-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJul-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJan-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJul-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJan-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMar-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJul-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMar-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNov-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNov-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMay-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSep-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMay-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMay-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMay-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJul-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSep-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSep-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDec-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; own calculations based on BBQ algorithm of NCER\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom the above results, it is observed that in general the peaks and troughs of actual realisations lead the expectation rather than the other way. The average duration of both the contractions and expansions was higher for the expectation as compared to actual (6.5 vs 6 periods in contractions and 4.38 vs 3.3 periods in expansions). The average cumulative movement of contractions is lower for expectation than actual (-13.73% vs -17.63%) and the average cumulative movement for the expansions is higher for the expectation than actual (8.31% vs 6.97%). This demonstrates that the SPF expectations are more persisting and are also averaging higher than actual realisations. As a result, the Professional Forecasters are overestimating inflation and have not been able to forecast turning points.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e6.4 Other Proxies\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eSPF-Long Term Expectation\u003c/strong\u003e\u003cp\u003eAs mentioned above, the SPF surveys included long term forecasts for 5 and 10 years between 2008 and 2017 for WPI and 2018 for CPI. As could be seen from Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the long-term expectations of the professionals seem anchored around the averages of 5.9 and 5.4 for the WPI and 6.9 and 6.0 for the CPI for the 5 and 10\u0026nbsp;year respectively, prior to the period of the FIT. These have shifted down to 4.2 and 4.3 for WPI and 5.3 and 5.0 for CPI respectively after the FIT. The results for the equations (1) and (4) for long term expectations for the 5 year forecasts for the data period July 2008 to April 2017 (WPI) and till April 2018 (CPI) are provided below. The 10-year expectation is not modelled as there were no sufficient observations for the forward term.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSummary of Model Coefficients-SPF Long Term expectations\u003c/span\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSPF-WPI-5yr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSPF-CPI-5 yr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward-looking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.49***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.38***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Form\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ewith dummy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ewith dummy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.56***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.71***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.04***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.08***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Expectations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.70***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDummy for FIT regime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.38***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.38***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003e*10%; ** 5%; *** 1%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIt can be seen that the coefficients for the forward expectations have wrong signs. The previous expectations and the constant are dominant in the formation of expectation, As the expectations are relatively stable, it can be said that these are anchored. The significance of dummy indicates the alignment of the professional economists to the Central Bank mandate on FIT.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsumer Confidence Index (CCI)\u003c/strong\u003e\u003cp\u003eSimilar to the other variables for expectations, we attempt to model the equations (1) and (4) on the CCI data for the period September 2012 to September 2023 as compared to CPI realisations. It is expected that the coefficients linking inflation to the net responses will have a negative sign, as explained therein. The results are given in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSummary of Model Coefficients-CCI -Inflation Expectations\u003c/span\u003e:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward-looking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 yr\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.48*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.82***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations (AR1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Form\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.90***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-30.01**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.46**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.77***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Expectations (AR1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.57***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWithout the 3 outliers in 2016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Form\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.59***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Realisations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.38**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Expectations (AR1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003e*10%; ** 5%; *** 1%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs can be observed, the findings are similar to the previous ones of IESH, viz., the constant and the previous expectations have dominated the expectation formation while the forward term was weak and/or not statistically significant. For the one-year expectation, the coefficient of previous realisation (adaptive term) was significant but small, thus reinforcing the conclusion that the consumer expectations are naively repetitive with absence of forward-looking features and no major learning attributes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e6.5 Discussion\u003c/h2\u003e\u003cp\u003eThe evidence of the backward looking and na\u0026iuml;ve, repetitive expectation formation of the households does not augur well for the expectations channel in India. Some researchers have maintained that households have exhibited adaptive expectations. Our results show that \u003cem\u003esuch adaptive behaviour is absent for the expectation of even current inflation\u003c/em\u003e. The claim of expectations getting anchored as they seem stable without relation to actual realisations, is not appealing since such anchoring seems to be at more than double of the actual realisation. \u003cem\u003eThere is more credence to the alternate explanation that there is a complete disconnect between policy pronouncements, actual realisations, and perceptions, despite heightened and transparent communication.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo the extent that the SPF expectations are in line with the anchoring concept, it is to be borne in mind that the professionals use similar frameworks as the Central Banks do, thus, \u0026ldquo;casting doubt on the amount of extra information that they provide\u0026rdquo; (Bernanke, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Professional Forecasters are also unable to forecast the turning points and are overestimating inflation. To the extent that there are forward looking and/or adaptive features in the professional expectations, it could well be a case of \u0026ldquo;Only financial market participants and professional forecasters seem to pay much attention to the actions of monetary policy-makers\u0026rdquo; (Coibion et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA few of the prior studies argued that since the other channels of transmission are weak, RBI should extract maximum impact through the expectations channel. This would tend to reinforce the circular argument of anchoring vs credibility; more importantly, the underlying causal factors for the inflation are downplayed when importance is given to the hazy concept of expectations. Interestingly, this runs against the prescription that it may be much better for the inflation talk to be off the radar so that there is no buildup of self-fulfilling behaviour (Rudd, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite all the shortcomings expectations are believed to provide some signals and \u0026ldquo;the authorities can afford to ignore such signals only if they are very confident that they are pure noise.\u0026rdquo; (Reis, 2022). However, at some stage if the policy keeps emphasising the anchoring of expectation so much, that it could actually become the overriding target despite the behaviour of actual inflation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIrrationality and bounded rationality\u003c/strong\u003e\u003cp\u003eThe presence of the expectations in the NKPC framework appears to have achieved the consensus on the long-term neutrality of money and the transmission of short-term real effects of shocks to the system. This compromise of sorts, is against the Knightian uncertainty highlighted by Keynes, that leads to herd behaviour and at best a limited interpretation of rationality in forming expectations. Because of such uncertainty, \u0026ldquo;\u0026hellip;.the facts of the existing situation enter, in a sense disproportionately, into the formation of our long-term expectations.\u0026rdquo;(Keynes, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1936\u003c/span\u003e). The mainstream macroeconomics, despite dubbing the framework as New Keynesian, \u0026ldquo;..however, seems to have taken a position directly antipodal to that of Keynes\u0026rdquo; (Nachane, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). And despite the evidence suggesting that the individuals are \u0026ldquo;...far from rational and forward looking...,\u0026rdquo; the canonical models adopted by the Central Banks continue to ignore the issue of money illusion (Schnabel, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStructural Nature of Inflation\u003c/strong\u003e\u003cp\u003eAs Bonatti et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) show, the effect of anchored expectations (that do not track the evolution of actual inflation) and rational expectations (that do) coincide when it is assumed that the shocks to prices and/or output are random in nature. However, when the source of inflation is considered structural in nature (Balakrishnan \u0026amp; Parameshwaran, 2019; and Kundurthi \u0026amp; Kalluru, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the inflation control is dependent on whether the policy prescriptions and/or the market forces lead towards resolving such structural issues. If not, the policy outcomes could well be sub-optimal. This is important, as the stated stance of no tolerance to inflation and credibility demands stern demand management with accompanying output sacrifices.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Concluding remarks","content":"\u003cp\u003eOur results show that the most important aspect of inflation expectations, viz., those of the Households are static and naively repetitive. The professional forecasters are observed to be anchored to the target inflation in the longer term and are adaptive in the shorter term. However, the professionals use similar models as the central bank, and hence there is little incremental policy guidance from the same. The professional forecasters also have been overestimating the inflation and are unable to forecast turning points. As the expectation is observed to be essentially backward looking, there is serious risk of central bank\u0026rsquo;s policies becoming rear-view driven. If it is believed that expectation drives long term inflation trend, it could lead to a persistence of tight money policies that jeopardize growth. Our empirical results reinforce the proposition that the actual sources of inflation need to be addressed rather than trying to gain an elusive policy credibility chasing a hazy target.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e***************\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eNo funding was availed for the paper. The authors report that there are no competing interests to declare\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalakrishnan, P., \u0026amp; Parameswaran, M. (2019, July 13). \u003cem\u003eThe Dynamics of Inflation in India\u003c/em\u003e. https://web.archive.org/web/20190713053721/http:/cds.edu/wp-content/uploads/2019/06/WP485.pdf\u003c/li\u003e\n\u003cli\u003eBehera, H. K., \u0026amp; Ranjan, A. (2024). \u003cem\u003eFood and Fuel Prices: Second Round Effects on Headline Inflation in India\u003c/em\u003e [RBI Bulletin]. https://rbidocs.rbi.org.in/rdocs/Bulletin/PDFs/03AR23042024E16411A151A74ADD9E23F6EC9DD5B168.PDF\u003c/li\u003e\n\u003cli\u003eBernanke, B. (2007, July 10). Inflation Expectations and Inflation Forecasting. \u003cem\u003eSpeech\u003c/em\u003e. Monetary Economics Workshop, NBER Summer Institute, Cambridge, Massachusetts. https://www.federalreserve.gov/newsevents/speech/bernanke20070710a.htm\u003c/li\u003e\n\u003cli\u003eBernanke, B. (2022, May 19). Inflation Expectations and Monetary Policy. \u003cem\u003eKeynote Address\u003c/em\u003e. Inflation Expectations: Determinants and Consequences, Spring 2022. https://www.youtube.com/watch?v=mV9sPolnFoI\u0026amp;t=2s\u003c/li\u003e\n\u003cli\u003eBonatti, L., Fracasso, A., \u0026amp; Tamborini, R. (2022). What to expect from inflation expectations: Theory, empirics and policy issues. \u003cem\u003ePublication for the Committee on Economic and Monetary Affairs, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament, Luxembourg, 2022\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eChinoy, S., Kumar, P., \u0026amp; Mishra, P. (2016). \u003cem\u003eWhat is Responsible for India\u0026rsquo;s Sharp Disinflation?\u003c/em\u003e (WP/16/166) [IMF Working Papers]. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/What-is-Responsible-for-Indias-Sharp-Disinflation-44175\u003c/li\u003e\n\u003cli\u003eClarida, R., Gal\u0026iacute;, J., \u0026amp; Gertler, M. (1999). The Science of Monetary Policy: A New Keynesian Perspective. \u003cem\u003eJournal of Economic Literature\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(4), 1661\u0026ndash;1707.\u003c/li\u003e\n\u003cli\u003eCoibion, O., Gorodnichenko, Y., Kumar, S., \u0026amp; Pedemonte, M. (2018). \u003cem\u003eInflation Expectations as a Policy Tool?\u003c/em\u003e (Working Paper 24788). National Bureau of Economic Research. https://doi.org/10.3386/w24788\u003c/li\u003e\n\u003cli\u003eDholakia, R. H., \u0026amp; Kadiyala, V. S. (2018). Changing Dynamics of Inflation in India. \u003cem\u003eEconomic and Political Weekly\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(9). https://www.epw.in/journal/2018/9/special-articles/changing-dynamics-inflation-india.html\u003c/li\u003e\n\u003cli\u003eFriedman, M. (1968). The Role of Monetary Policy. \u003cem\u003eThe American Economic Review\u003c/em\u003e, \u003cem\u003eLVIII\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eGerberding, C. (2001). \u003cem\u003eThe Information Content of Survey Data on Expected Price Developments for Monetary Policy\u003c/em\u003e. https://doi.org/10.2139/ssrn.2785125\u003c/li\u003e\n\u003cli\u003eGoodhart, C. (2021, September 28). The Future of Inflation. \u003cem\u003eBeyond the Pandemic: The Future of Monetary Policy\u003c/em\u003e. ECB Forum on Central Banking, 2021. https://www.ecb.europa.eu/pub/pdf/sintra/ecb.forum_central_banking.202112~3d9f018812.en.pdf\u003c/li\u003e\n\u003cli\u003eGoyal, A. (2016). Unconventional monetary policy in emerging markets. \u003cem\u003eMacroeconomics and Finance in Emerging Market Economies\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 101\u0026ndash;108. https://doi.org/10.1080/17520843.2016.1180835\u003c/li\u003e\n\u003cli\u003eGoyal, A., \u0026amp; Parab, P. (2019a). Modeling heterogeneity and rationality of inflation expectations across Indian households. \u003cem\u003eIndira Gandhi Institute of Development Research, Mumbai Working Papers\u003c/em\u003e, Article 2019\u0026ndash;02. https://ideas.repec.org//p/ind/igiwpp/2019-02.html\u003c/li\u003e\n\u003cli\u003eGoyal, A., \u0026amp; Parab, P. (2019b). \u003cem\u003eInflation Convergence and Anchoring of Expectations in India\u003c/em\u003e. Indira Gandhi Institute of Development Research. http://www.igidr.ac.in/working-paper-inflation-convergence-anchoring-expectations-india/\u003c/li\u003e\n\u003cli\u003eKeynes, J. M. (1936). \u003cem\u003eGeneral Theory Of Employment , Interest And Money\u003c/em\u003e (2016th\u0026ndash;04 ed.). Atlantic Publishers \u0026amp; Dist.\u003c/li\u003e\n\u003cli\u003eKundurthi, R., \u0026amp; Kalluru, S. R. (2023). \u003cem\u003eInflation Dynamics in India\u0026mdash;A Structural View\u003c/em\u003e (SSRN Scholarly Paper 4604642). https://doi.org/10.2139/ssrn.4604642\u003c/li\u003e\n\u003cli\u003eNachane, D. M. (2018). \u003cem\u003eCritique of the New Consensus Macroeconomics and Implications for India\u003c/em\u003e. Springer. https://econpapers.repec.org/bookchap/sprisbuec/978-81-322-3920-8.htm\u003c/li\u003e\n\u003cli\u003eNCER. (n.d.). \u003cem\u003eNCER - Data and code\u003c/em\u003e. NCER. Retrieved May 14, 2024, from http://ncer.edu.au/resources/data-and-code.php\u003c/li\u003e\n\u003cli\u003ePatra, M. D., \u0026amp; Ray, P. (2010). \u003cem\u003eInflation Expectations and Monetary Policy in India: An Empirical Exploration\u003c/em\u003e (IMF Working Papers). https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Inflation-Expectations-and-Monetary-Policy-in-India-An-Empirical-Exploration-23719\u003c/li\u003e\n\u003cli\u003ePattanaik, S., Muduli, S., \u0026amp; Ray, S. (2020). Inflation expectations of households: Do they influence wage-price dynamics in India? \u003cem\u003eMacroeconomics and Finance in Emerging Market Economies\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 244\u0026ndash;263. https://doi.org/10.1080/17520843.2020.1720264\u003c/li\u003e\n\u003cli\u003ePhelps, E. S. (1967). Phillips Curves, Expectations of Inflation and Optimal Unemployment over Time. \u003cem\u003eEconomica\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(135), 254\u0026ndash;281. https://doi.org/10.2307/2552025\u003c/li\u003e\n\u003cli\u003eRudd, J. B. (2021). \u003cem\u003eWhy Do We Think That Inflation Expectations Matter for Inflation? (And Should We?)\u003c/em\u003e (Finance and Economics Discussion Series (FEDS)) [Working Paper]. Board of Governors of Feederal Reserve System. https://www.federalreserve.gov/econres/feds/why-do-we-think-that-inflation-expectations-matter-for-Inflation-and-should-we.htm\u003c/li\u003e\n\u003cli\u003eSaakshi, \u0026amp; Sahu, S. (2019). An analysis of heterogeneity in inflation expectations across cities in India. \u003cem\u003eJournal of Economic Studies\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(5), 1116\u0026ndash;1136. https://doi.org/10.1108/JES-08-2018-0297\u003c/li\u003e\n\u003cli\u003eSchnabel, I. (2020, November 24). COVID-19 and monetary policy: Reinforcing prevailing challenges. \u003cem\u003eSpeech\u003c/em\u003e. The Bank of Finland Monetary Policy webinar: New Challenges to Monetary Policy Strategies, Frankfurt. https://www.ecb.europa.eu/press/key/date/2020/html/ecb.sp201124~bcaebee7c0.en.html\u003c/li\u003e\n\u003cli\u003eSharma, N. K., \u0026amp; Bicchal, M. (2018). The properties of inflation expectations: Evidence for India. \u003cem\u003eEconomiA\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 74\u0026ndash;89. https://doi.org/10.1016/j.econ.2017.12.002\u003c/li\u003e\n\u003cli\u003eSingh, D. P., Mishra, A., \u0026amp; Shaw, P. (2022). Taking Cognisance of Households\u0026rsquo; Inflation Expectations in India. \u003cem\u003eRBI -DEAP Research\u003c/em\u003e. https://www.rbi.org.in/Scripts/PublicationsView.aspx?id=20991\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Though formal legislation was done in 2016, the policy documents suggest adoption from 2014 onwards, also evidenced by the shifting of object of reference of surveys from CPI-IW to CPI.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The algorithm uses parameters such as symmetric window (period on either side of the peak/trough), phase (number of periods of expansion/contraction), cycle (number of periods from peak to peak) and a threshold percent change to qualify for directional turn. The study used a minimum phase of 2 periods, minimum cycle of 3 periods, symmetric window of 1 period and a threshold level of 15%.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Gokhale Institute of Politics and Economics","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Inflation Expectations, Flexible Inflation Targeting, Forward Looking, Monetary Policy","lastPublishedDoi":"10.21203/rs.3.rs-7158381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7158381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePost adoption of the Flexible Inflation Targeting by India, the importance of anchoring inflation expectations has been taking centre stage of academic and policy research. The expectations channel is now considered as an important transmission mechanism in itself. This paper explores whether expectation formation in the Indian context can be treated as forward or backward looking with or without adaptive features, using the data from the available surveys. It is observed that the inflation expectation formation of households is predominantly naïve and repetitive and hence does not provide any additional information for policy guidance. Professional Forecasters are also unable to forecast the turning points and are overestimating inflation, thus questioning the policy relevance of such expectations.\u003c/p\u003e\n\u003cp\u003eJEL classifications: E3, E5, E7\u003c/p\u003e","manuscriptTitle":"Inflation Expectations and Policy Relevance in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 13:17:56","doi":"10.21203/rs.3.rs-7158381/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b388cf37-c0e6-4921-b7ca-e0ac08887660","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51758172,"name":"Macroeconomics"}],"tags":[],"updatedAt":"2025-07-21T13:17:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-21 13:17:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7158381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7158381","identity":"rs-7158381","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}