Exchange prices shocks and inflation rate persistence for north African countries: a fractional cointegration relationship

Exchange prices shocks and inflation rate persistence for north African countries: a fractional cointegration relationship | 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 Exchange prices shocks and inflation rate persistence for north African countries: a fractional cointegration relationship Rachid BENKHELOUF, Abdelkader SAHED This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4308266/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 This study aims to analyse the relationship between exchange rate and inflation rates persistence in North African countries (Algeria, Tunisia, and Morocco) to determine the extent of inflation persistence in face to exchange rate shocks from January 1987 to April 2023 using the recently developed Fractional Cointegration Model. This topic was chosen due to its significance for monetary policymakers, investors, financial analysts and academics in understanding the dynamics of inflation persistence in North African countries. Our results also showed that Algeria, Tunisia and Morocco has a co-integration relationship between exchange rate and the inflation rate, As exchange rate shocks cause permanent inflation persistence in Algeria and the variation in the inflation rate in Tunisia will persistence for a longer period due to the shock of exchange rate before eventually fading away and the change in the persistence of inflation rate in a Morocco due to an exchange rate shock will fade away within a short period. Econometrics inflation rate persistence exchange rate shocks fractional cointegration North African countries Figures Figure 1 1. INTRODUCTION Inflation is considered a macroeconomic problem for all countries around the world, without exception, due to its negative effects on economic expansion and income redistribution. Therefore, achieving an acceptable inflation rate is one of the economic goals of any central bank in any country. This task requires a deep understanding of inflation dynamics such as the inflation rate (Tule et al., 2020 ). Understanding the dynamics of inflation persistence will help central bank officials to make appropriate monetary policy decisions (Amano, 2007 ; Coenen, 2007 ; Tetlow, 2019 ). Inflation persistence is defined as the time it takes for the shocks to inflation to dissipate (Sbordone, 2007 ), and it can also be defined as the speed at which inflation returns to its equilibrium level (medium and long term) after a shock (Bilici & Çekin, 2020 ). The effectiveness of monetary policy strategy is determined by its ability to achieve a low level of inflation persistence, indicating that shocks to inflation are eliminated within a short period (Bratsiotis et al., 2015 ; Gerlach & Tillmann, 2012 ; Meller & Nautz, 2012 ). (Batini & Nelson, 2001 ) Inflation persistence is sometimes defined as the tendency for price shocks to push inflation away from its stable state, including the inflation target, for an extended period. Persistence is important because it affects the costs of production in reducing inflation to the target. It is often described as a "sacrifice ratio," where a lower level of persistence implies a larger policy space. Policy space refers to the ability of monetary policy to absorb temporary price shocks. Countries with high persistence and low policy space may need to adjust macroeconomic policies materially to accommodate price shocks, as they impact overall inflation and inflation expectations over a sustained period (Roache, 2014 ). The effectiveness of monetary policy strategy lies in its ability to achieve low inflation persistence which means that inflation rate shocks can be quickly eliminated (Bratsiotis et al., 2015 ; Gerlach & Tillmann, 2012 ; Meller & Nautz, 2012 ; Misati et al., 2013 ). On the contrary, the wrong appreciation of the inflation rate persistence can be charged with monetary policy makers. (Batini & Nelson, 2001 ) distinguished between three types of persistence: Positive serial correlation in inflation This refers to the positive relationship between current and lagged inflation rates, indicating a persistent pattern of inflationary movements; Time lags in the effects of systematic monetary policy measures on inflation (peak effects) This refers to the delays between the implementation of systematic monetary policy actions and their impact on inflation It highlights the lagged response of inflation to policy measures; Delayed responses of inflation to non-systematic policy measures (such as policy shocks) This type of persistence refers to the delayed effects of non-systematic policy actions on inflation. It captures the delayed response of inflation to unexpected policy shocks. In addition, there is another type of persistence mentioned by (Fuhrer & Moore, 1995 ), which is the inflation response to its own shocks. This type of persistence emphasises how inflation responds to its own disturbances. Exchange rate fluctuations are an important driver of inflation and therefore could have significant implications for determining the nature of monetary policy (Fischer, 2015 ; Forbes, 2016 ; Mishkin, 2008 ). Price shocks, including exchange rate shocks, are considered external shocks that impact inflation rates in any country. Numerous studies have been conducted on the determinants of inflation persistence, but little attention has been given to the role of exchange rates as a key indicator of inflation. The expected impact of currency movements on consumer prices will determine how the central bank responds. In particular, monetary authorities may look beyond this but may choose to respond if the impact on inflation is persistent (Ball & Reyes, 2008 ; Calvo & Reinhart, 2002 ). Therefore, an increase in the country's persistent inflation rate as a result of an exchange rate shock indicates a weakness in the effectiveness of its monetary policy. This calls for a review of the monetary policy in response to the exchange rate shock. On the other hand, if the inflation rate continues to decline or remain unchanged due to an exchange rate shock. It means that the current monetary policy of the country is responding to the exchange rate shock and there is no need to review the monetary policy in the face of such shock. Monetary policy responding to price shocks in a more accommodative manner is likely to produce more persistent inflation. For that reason, failure to accommodate inflation shocks is frequently perceived as a precondition for lower inflation persistence (Alogoskoufis & Smith, 1991 ). The importance of studying inflation persistence lies in its significant role in shaping monetary policy. It determines the extent to which monetary authorities can maintain a stable level of output and inflation simultaneously, thus influencing the performance of monetary policies (Antonakakis et al., 2016 ). The main objective of our study is to investigate the impact of exchange rate shocks on the inflation rate persistence in North African countries (Algeria, Tunisia and Morocco). Therefore, this study is highly important for the monetary authorities in these countries to determine whether they should review their monetary policy in the face of global exchange rate shocks. We will use the Fractional Cointegration Vector Autoregressive (FCVAR) model, introduced by (Sø. Johansen, 2008 ) and further developed by (S. Johansen & Nielsen, 2012 ), instead of the traditional Cointegrated Vector Autoregressive (CVAR) model proposed by (S. Johansen, 1995 ). Both models capture the long-term relationship between variables, but the CVAR model assumes only two cases of long-term relationship indicated by zero-order of integration I(0) or first-order of integration I(1), while the FCVAR model allows for different orders of integration (I(d)), where d represents any real-valued order of integration, enabling long memory persistence (1 < d < 0) (L. A. Gil-Alana et al., 2017 ; L. Gil-Alana & Carcel, 2020 ; Granville & Zeng, 2019 ). For more details, we chose the FCVAR model instead of the CVAR model because several studies have found that the inflation rate is fractional integrated see for example: (Bilici & Çekin, 2020 ; Granville & Zeng, 2019 ; Tule et al., 2020 ). The same applies to exchange rate. This makes the FCVAR approach more suitable for analyzing the long-term relationship between exchange rates and inflation. This study will contribute to the empirical literature analyzing the determinants of inflation persistence in North African countries (Algeria, Tunisia and Morocco) by examining the impact of an external factor represented by exchange rates using the recently developed Fractional Cointegration Vector Autoregressive (FCVAR) model. It will also shed light on the role of the North African country's monetary policy in modeling this impact. The organization of this study after this introductory section is as follows: Section 2 reviews the existing literature and evaluates its findings and highlights the added value of our study, Section 3 presents the data and its temporal evolution, Section 4 outlines the methodological framework of the study, Section 5 presents the results and discussion and Finally, Section 6 concludes the paper. 2. Literature Review The inflation persistence is the time it takes for inflation rate shocks to dissipate (Sbordone, 2007 ), and it can also be defined as the speed at which inflation rate returns to its long-term equilibrium level after a shock (Bilici & Çekin, 2020 ). Understanding the nature of inflation persistence will assist authorities and policy makers in implementing effective monetary policies towards maintaining price stability and economic stability (Bernanke et al., 1997 ). Numerous studies have been conducted in this field, which can be categorized into three types. Firstly studies that believe inflation persistence dynamics are determined by the characteristics of the inflation rate; These studies employ techniques such as single-variable autoregressive modeling, including tests for stationary, partial integration, fractional unit roots, or time-varying parameter estimation. There are consistent findings of these studies, for example The study by (Bilici & Çekin, 2020 ) used a time-varying parameter (TVP) estimation model to examine inflation persistence in Turkey. They found that inflation persistence increases and exhibits high volatility during periods of high inflation. (Bratsiotis et al., 2015 ) study explored the role of the inflation target in inflation persistence in seven countries. Their findings revealed that inflation targets significantly reduce inflation persistence. (Darvas & Varga, 2014 ) study in which they analyzed the dynamics of inflation persistence in Central and Eastern European countries and found that inflation persistence tends to be higher in times of high inflation. (Gerlach & Tillmann, 2012 ) study in which they analyzed the persistence of inflation in Asia-Pacific countries before and after the introduction of inflation targeting. They found that the speed at which persistence declines varies from one country to another and that persistence tends to decline after the adoption of inflation targeting. (Granville & Zeng, 2019 ) Secondly, some studies give an important role to inflation expectations in determining the inflation persistence, where the continuity of inflation based on expectations is driven by differences between public perceptions about the inflation target and the real inflation target (explicit or implicit) set by the central bank. Simple and brief auto-regression analyzes are used to predict inflation and this type. One of the models makes the degree of perceived and actual inflation persistence varies over time and a function of the history of shocks, For example we mention the study of (Erceg & Levin, 2003 ) which shows how identification of the inflation target set by the central bank can explain the process of gradual deflation during the Volcker era in a purely forward-looking model. Likewise, When private agents have incomplete information about whether a given disturbance in inflation is due to a temporary or permanent supply shock, inflation expectations may adjust only gradually after a purely temporary shock, making the inflationary response more persistent. A study by (Orphanides & Williams, 2005 ) in which they explain that active central banks that are very concerned with stabilizing the output gap may slow down the learning process of agents trying to predict inflation and thus may increase the continuity of the inflation process. In this case, the monetary policy system will affect the formation of inflation expectations and may effect on the continuity of inflation through this channel. In general, the presence of a credible political system that focuses on price stability would reduce the continuity of inflation. The study of (Gaspar et al., 2006 ) that found a clear map between the monetary policy system and the distribution of the stability parameter in the perceived law of motion of inflation such that the continuous responses to cost-increasing shocks and the stability of inflation expectations resemble the ideal policy under commitment and rational expectations and in this case it depends on the expectations of policy actions. Impacting future inflation expectations. (Granville & Zeng, 2019 ) investigated the dynamics of inflation persistence in the United States and concluded that the dynamics of inflation persistence are related to expectations shaped by memories of past inflation. (Zhang, 2011 ) study investigated the relationship between inflation persistence, inflation expectations and monetary policy in China. The study suggests that the dynamics of inflation expectations can be related to structural change in inflation, this finding implies that the quiescence of inflation in China over the past decade could well be followed by a return to a high inflation era in the absence of a determined effort by the monetary authorities in managing inflation expectations. Therefore, the use of a preemptive monetary policy to anchor inflationary expectations and to keep inflation moderate is warranted in China. (Antonakakis et al., 2016 ) study that the degree of inflation persistence from the estimated long memory parameter is relatively small when considering online price indices as a measure of inflation and this indicates a higher level of efficiency of monetary policies in managing the prices. Third other studies suggest that inflation persistence can be influenced by structural economic factors such as progressive taxation, human capital and the monetary policy framework, particularly inflation targeting or exchange rate regimes. These studies have yielded different results, for example The study by (Oloko et al., 2021a ) aimed to analyze the relationship between the gold price and inflation rate using the fractional cointegration model to determine the extent of inflation persistence in the face of gold price shocks in some advanced and developing countries. The results indicated that the impact of gold price shocks remains significant for a long period in relation to inflation persistence in developing countries and for a short period in relation to inflation persistence in advanced countries. The study by (Oloko et al., 2021b ) investigated the effect of oil price shocks on inflation persistence among the top ten oil-exporting and oil-importing countries using the fractional cointegration VAR model. The results showed that the inflation persistence in both oil-exporting and oil-importing countries is not significantly increased due to oil price shocks, indicating that the monetary policies of these countries accommodate oil price shocks. A study (Geronikolaou et al., 2020 ) that investigated the effect of progressive taxes and human capital on the persistence of inflation in 28 OECD countries, where they found that increasing progressive taxes reduces the spread of shocks and thus increases the persistence of inflation. The dispersion of human capital across sectors acts as a barrier to labor mobility and thus further inflation rigidity through the same channel. The study of (Wu & Wu, 2018 ) examined the role of the flexible exchange rate system in continuing inflation using 23 industrialized countries, and its results concluded that there is ambiguity in the effect of the exchange rate system on the continuity of relative inflation; Floating compared to bond rates. A Study (Canarella & Miller, 2016 ) who analyzed the relationship between inflation targeting and inflation persistence in selected developed countries (Canada, Sweden, United Kingdom) and industrialized and newly emerging economies (Chile, Israel, and Mexico) that adopted inflation targeting (information technology) before 2000. They concluded Overall results were mixed and varied according to the level of development in countries. Specifically, The inflationary processes in the three advanced economies were partially integrated, steady, medium-yielding, and share a common inflationary persistence. Whereas, the inflationary processes in the three emerging market economies were partially integrated, medium return, and unstable. The study conducted by (Salisu et al., 2017 ) found mixed short-term and similar long-term relationships between oil prices and inflation rates in both oil-importing and oil-exporting countries. This indicates that the empirical result of the impact of oil price shocks on inflation rate persistence in oil-importing and oil-exporting countries may be similar or divergent. Study by (Kilian & Park, 2009 ), This study investigated the role of oil price shocks in inflation persistence in a group of industrialized countries. It found that oil price shocks have a substantial impact on inflation persistence, with the effect varying across countries. The study by (Mishkin & Schmidt-Hebbel, 2007 ) examined the impact of the monetary policy framework on inflation persistence and showed that medium-term inflation targeting reduces inflation persistence more than fixed-money targeting. A Study by (Burdekin & Siklos, 1999 ), Using long time series data from the United Kingdom, the United States, Canada, and Sweden, we suggest that these authors' emphasis on a post-1967 shift in inflation persistence is misplaced and that there are other equally good candidates to account for changes in inflation persistence such as wars, oil price shocks, and central bank reforms. A Study by (Bleaney & Francisco, 2005 ) Using data for 102 developing countries, it is shown that inflation persistence is particularly low in countries on hard pegs, and particularly high in countries with severe inflationary problems. Inflation persistence is similar under floating and soft pegs. A study by (Giannellis & Koukouritakis, 2013 ) tests the conjecture that inflation is persistent in selected countries in Latin America and, by establishing an appropriate nonlinear model, finds no strong evidence in favor of the above generally accepted view. However, the available evidence shows that in periods of significant decline in the value of the domestic currency, the domestic inflation rate was persistent, whereas in periods of slower decline, or relative stability, it was temporary. This study falls within the category of studies that consider structural factors as additional determinants of inflation rate persistence. It focuses on an external factor, which is exchange rates, in contrast to the local factors addressed in previous studies. Moreover, it focuses specifically on North African countries (Algeria, Tunisia, and Morocco), as no study on the inflation rate persistence has yet been conducted in these countries, especially given their divergent economic characteristics. Additionally, the study utilizes the Fractional Co-integration model as a new and developed standard approach. Lastly, to the best of our knowledge, this article is one of the initial attempts to study the relationship between exchange rates and inflation rate persistence in North African countries, which serves as a motivation for undertaking this study. 3. Data In this study, we use monthly data for each of the exchange rate of North African countries (Algeria, Tunisia and Morocco) measured in the currency of each country in relation to the US dollar, and the inflation rate expressed in consumer prices measured in the currency of each country from January 1987 to Avril 2023. This dataset comprises 436 observations, covering periods of both high and low inflation rates in North African countries, as well as positive and negative exchange rate shocks, We represent an exchange rate as "ER" and consumer prices as "CPI.", We represent the countries of Algeria, Tunisia, and Morocco as "ALG," "TUN," and "MOR," respectively. All data were obtained from the World Bank website: https://databank.worldbank.org/ Table 1 Descriptive Statistics Variable CPI ER ALG TUN MOR ALG TUN MOR observations 436 436 436 436 436 436 Mean 88.34451 94.00166 90.03239 69.16928 1.525530 9.099816 Median 80.32727 82.70202 90.62810 72.42811 1.321404 8.946609 maximum 206.8364 205.5556 130.0221 146.4752 3.270952 11.87833 minimum 11.15458 36.99495 50.19254 3.857635 0.786095 7.290864 Std. Dev 50.47340 40.86194 19.58851 37.33886 0.661314 0.870491 Jarque-Bera 16.16687 47.94254 20.73867 5.777720 98.09545 46.83012 bropability 0.000309 0.000000 0.000031 0.055640 0.000000 0.000000 Source: Computed by The authors Figure 1 presents the trends in exchange rate and consumer prices for North African countries. From the figure, some degree of co-movement between observed consumer prices and exchange rate can be observed, which suggests the presence of cointegration. More Algeria and Tunisia appear to exhibit a higher degree of co-movement than Morocco. This suggests that these countries (Algeria and Tunisia) may exhibit higher exchange rate-inflation cointegrating persistence than Morocco. Table 1 presents the descriptive statistics for exchange rate and consumer prices for the North African countries. The table shows that the average consumer prices and standard deviation for Algeria is 88.34 percent and 50.47 percent, respectively; And Tunisia is 94 percent and 40.86 percent, respectively, and Morocco is 90.03 percent and 19.58 percent, respectively. This suggests that inflation rate in North African countries is higher and more volatile. The table also shows that the average exchange rate and standard deviation for Algeria is 69.16 percent and 37.33 percent, respectively; and Tunisia is 1.32 percent and 0.66 percent, respectively, and Morocco is 8.94 percent and 0.87 percent, respectively. This suggests that the exchange rate in Algeria is higher and more volatile compared to the exchange rate dynamics of Morocco and Tunisia This latter is the lowest in exchange rate. 4. METHODS 4.1. Fractional integration approaches Here is a brief description of the method that will be adopted in this study, which is the recently developed Fractional Cointegration Vector Auto-regression (FCVAR) model by (S. Johansen & Nielsen, 2012 ). This model has gained significant popularity, as evident from its recent application in various fields of study, including (Solarin Sakiru Adebola et al., 2019 ), (Aye et al., 2017 ), (L. A. Gil-Alana et al., 2017 ), (L. Gil-Alana & Carcel, 2020 ), (Nielsen & Shibaev, 2018 ), (Tule et al., 2020 ), and (Yaya et al., 2019 ), This model allows for fractional integration with non-zero orders when the series is not integrated at the zero order (L. Gil-Alana & Carcel, 2020 ). The latter provides researchers with evidence of when transience is found to be reasonable, rather than assuming the explicit nature of permanent shocks that require a long time to fade away. Therefore, we will apply the framework of fractional integration to consumer prices in the North African countries and exchange rates. To explain fractional integration, we define the typical process I(d) in Eq. ( 1 ) below. $$(1-L{)}^{d}{x}_{t}={u}_{t},t=0,\pm 1,\dots$$ 1 Where d can be any real value, L is the lag-operator (Lxt = xt-1) and ut is I(0), defined as a covariance stationary process with a spectral density function that is positive and finite at the zero frequency. The idea of fractional integration was introduced by (C. W. Granger, 1981 ; C. W. J. Granger, 1980 ; C. W. Granger & Joyeux, 1980 ; Hosking, 1981 ), although (Adenstedt, 1974 ) had already given evidence of its meaning. The polynomial (1) Ld in (1) can be formulated in terms of its binomial expansion in such a way that for all real d, $$(1-L{)}^{d}=\sum _{j=0}^{\infty } {\psi }_{j}{L}^{j}=\sum _{j=0}^{\infty } \left(\begin{array}{c}d\\ j\end{array}\right)(-1{)}^{j}{L}^{j}=1-dL+\frac{d(d-1)}{2}{L}^{2}-\dots ,$$ 2 And thus: $$(1-L{)}^{d}{x}_{t}={x}_{t}-d{x}_{t-1}+\frac{d(d-1)}{2}{x}_{t-2}-\dots$$ 3 Implying that Eq. ( 1 ) can be expressed as $${x}_{t}=d{x}_{t-1}-\frac{d(d-1)}{2}{x}_{t-2}+\dots +{u}_{t}$$ 4 Given the parameterization in (1) we can distinguish different cases depending on the value of d. If d = 0, xt = ut, xt is said to be “short memory” or I(0); if d > 0, xt is said to be “long memory”, because of the strong association between observations which are far in time. Here, if d belongs to the interval (0, 0.5) then xt is still covariance stationary, while d ≥ 0.5 implies non-stationarity. Finally, if d < 1, the series is mean reverting, meaning that the effect of shocks will eventually disappear in the long run, contrary to what happens if d ≥ 1, with shocks persisting forever (L. Gil-Alana & Carcel-Villanova, 2018 ). 4.2. The fractionally cointegration VAR model The theoretical derivation of the fractional integration model has been shown in the previous section. As a univariate model, this includes one fractional integration parameter, d, which describes the order of integration of either consumer prices or exchange rates. As exchange rates and consumer prices are fractionally integrated, the next is to determine evidence of fractional cointegration between the two variables. In other words, This is to determine the existence of common fractional integration between exchange rates and consumer prices in the North African countries As the differencing parameter, d, an integer value, was not restricted by (Engle & Granger, 1987 ), (Robinson, 2008 ) introduced the fractional cointegration technique which allows for simultaneous estimation of the differencing parameter d as well as other parameters in the relationship. In a broader sense, given two real numbers d, b, the components of the vector zt are said to be cointegrated of order d, b, denoted zt ∼ CI(b, d) if: All the components of zt are I(d) There exists a vector α ∕= 0 such that st = α ′zt ∼ I(γ) = I(d − b), b > 0 Where α is the cointegrating vector and st is the error term (Aye et al., 2017 ). In the multivariate model specification we start our model specification with the CVAR model and after that the FCVAR model since the latter is a fractional modification of the first, The CVAR model is: $$\varDelta {Y}_{t}=\alpha {\beta }^{{\prime }}{Y}_{t-1}+\sum _{i=1}^{k} {\varGamma }_{i}\varDelta {Y}_{t-i}+{\epsilon }_{t}=\alpha {\beta }^{{\prime }}L{Y}_{t}+\sum _{i=1}^{k} {\varGamma }_{i}\varDelta {L}^{i}{Y}_{t}+{\epsilon }_{t}$$ 5 The simplest way to derive the FCVAR model is to replace the difference and lag operators and L in (2) by their fractional counterparts, b and Lb = 1 b, respectively. We then obtain $${{\Delta }}^{b}{Y}_{t}=\alpha {\beta }^{{\prime }}{L}_{b}{Y}_{t}+\sum _{i=1}^{k} {{\Gamma }}_{i}{{\Delta }}^{b}{L}_{b}^{i}{Y}_{t}+{\epsilon }_{t}$$ 6 Which is applied to Y X t = d b t such that $${{\Delta }}^{d}{X}_{t}=\alpha {\beta }^{{\prime }}{L}_{b}{{\Delta }}^{d-b}{X}_{t}+\sum _{i=1}^{k} {{\Gamma }}_{i}{{\Delta }}^{b}{L}_{b}^{i}{Y}_{t}+{\epsilon }_{t}$$ 7 Where ℇt is p-dimensional independent and identically distributed, with mean zero and covariance matrix Ω. The parameters have the usual interpretations of the CVAR model. Thus, α and β are p × r matrices, where 0 ≤ r ≤ p. The columns of β are the cointegrating relationships in the system, that is to say the long-run equilibria. The parameters Γi govern the short-run behavior of the variables and the coefficients in represent the speed of adjustment towards equilibrium for each of the variables. The FCVAR model permits simultaneous modeling of the long-run equilibria, the adjustment responses to deviations from the equilibria and the short-run dynamics of the system (L. A. Gil-Alana et al., 2017 ; L. Gil-Alana & Carcel-Villanova, 2018 ). The CVAR model is a special case of the FCVAR model that has this relationship, d = b = 1. To derive implication for inflation persistence from the fractional cointegration between inflation and exchange rate, which will be interesting to monetary authorities, the vector of endogenous variables was normalized on consumer prices in this study, such that we have: $${\text{ }\text{CPI}\text{ }}_{\text{t}}=\alpha +\beta {\text{ ER }}_{\text{t}-\text{k}}+{x}_{t},(1-\text{L}{)}^{\text{d}}{x}_{t}={\text{u}}_{\text{t}},\text{t}=\text{1,2},\dots ,$$ 8 Where the parameter d indicates the degree of persistence, β is now an indicator of the effect of the present (and past) exchange rate on domestic consumer prices of the respective countries (L. A. Gil-Alana et al., 2017 ). The differencing parameter, d, in the cointegrating equation, Eq. ( 8 ), relies on equality between fractional integration of consumer prices and exchange rates (dCPI = dER). Hence, d in Eq. ( 8 ) is the fractional cointegrating persistence, which can explain the effect of shocks to the exchange rate of inflation persistence. The fractional cointegration between exchange rate and the consumer prices describes how exchange rate shocks can affect the persistence of inflation in four cases depending on the values of the cointegration difference coefficient d: The first case is where (d = 0) the cointegration process is constant and has a short memory with no cointegration continuity. This means that the change in the inflation rate persistence due to the exchange rate shock will vanish almost immediately, in other words the effect of the exchange rate shocks on the inflation rate persistence does not persist. The second case is (0 < d < 0.5) The cointegration process is also stationary, but it shows a long memory with low persistence in the cointegration This indicates that the effect of the exchange rate shock on the inflation rate persistence in a country will last for a short period. In other words, the change in the persistence of inflation rate in a country due to an exchange rate shock will fade away within a short period. The third case is (0.5 < d < 1) the cointegration process is also highly stable, but it shows a long memory with a high continuity in the cointegration, meaning that the change in the inflation rate persistence due to the shock of exchange rate will continue for a longer period before it finally fades. The fourth case is (d ≥ 1), which means that the variance is not fixed, as exchange rate shocks cause permanent inflation to continue (Aye et al., 2017 ; L. A. Gil-Alana et al., 2017 ; Tule et al., 2020 ). When ensuring that each series is fractional integrated, the FCVAR model estimation is performed in four steps: First Determine the optimal delay length model; Second Determine the degree of integration; Third Partial cointegration test using specified optimal delay and cointegration order; Fourth testing the model residuals for serial correlation; And Finally A comparison between the FCVAR model and the CVAR model using the probability ratio [LR] test. 5. RESULTS 3.1. Stationarity of Data By conducting Dickey-Fuller (ADF) and Philips-Perron (PP) tests, the results came as shown in Table No. 02, where we do not reject the hypothesis that there is a unit root in each of the time series of the consumer prices and the exchange rate for all countries (Algeria, Tunisia and Morocco), since the “t” statistics Greater than critical values at all levels of conventional significance. The probabilities also show that the unit root hypothesis is not rejected for the consumer prices and the exchange rate, the non-stationarity of the time series allows further tests for the cointegration. Table 2 Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) test results Phillips–Perron test (PP) At Level CPI ER ALG TUN MOR ALG TUN MOR With Constant t-Statistic Prob 3.759858 1.000000 no 9.631413 1.000000 no -0.255183 0.928397 no -0.4199 0.9029 no 1.1054 0.9976 no -2.2228 0.1985 no With Constant & Trend t-Statistic Prob 1.880763 0.999998 no 10.74666 1.000000 no -1.538006 0.815153 no -1.4232 0.8531 no -1.0315 0.9373 no -2.3109 0.4266 no Without Constant & Trend t-Statistic Prob 9.789520 1.000000 no 14.69340 1.000000 no 6.648119 1.000000 no 2.9986 0.9994 no 2.9106 0.9992 no 0.1244 0.7214 no Augmented Dickey-Fuller test (ADF) At Level CPI ER ALG TUN MOR ALG TUN MOR With Constant t-Statistic Prob 3.5395 1.0000 no 3.8651 1.0000 no -0.2459 0.9297 no -0.5101 0.8862 no 0.7569 0.9932 n0 -2.6733 0.0795 * With Constant & Trend t-Statistic Prob 1.7353 1.0000 no 10.6393 1.0000 no -1.4710 0.8382 no -1.5193 0.8218 no -1.1524 0.9176 no -2.7465 0.2184 no Without Constant & Trend t-Statistic Prob 2.9983 0.9994 no 3.3121 0.9998 no 7.1863 1.0000 no 2.7674 0.9988 no 2.5739 0.9978 no 0.0438 0.6963 no Notes: (*) Significant at the 10% and (no) Not Significant *MacKinnon (1996) one-sided p-values. Source: Computed by The authors 5.2. Fractional integrated model estimation By conducting Dickey-Fuller (ADF) and Philips-Perron (PP) tests, the results came as shown in Table No. 02, where we do not reject the hypothesis that there is a unit root in each of the time series of the consumer prices and the exchange rate for all countries (Algeria, Tunisia and Morocco), since the “t” statistics Greater than critical values at all levels of conventional significance. The probabilities also show that the unit root hypothesis is not rejected for the consumer prices and the exchange rate, the non-stationarity of the time series allows further tests for the cointegration. Table 3 Fractional integration estimates based on local Whittle estimator and GPH test variable m Local Whittle Estimator GPH test ALG TUN MOR ALG TUN MOR CPI T 0.6 T 0.7 T 0.8 0.976689 ∗∗∗ (0.0811107) 0.968422 ∗∗∗ (0.0597614) 0.94761 ∗∗∗ (0.0440225) 0.953731 ∗∗∗ (0.0811107) 0.962333 ∗∗∗ (0.0597614) 0.948778 ∗∗∗ (0.0440225) 0.949635 ∗∗∗ (0.0811107) 0.947989 ∗∗∗ (0.0597614) 0.94613 ∗∗∗ (0.0440225) 0.976783 ∗∗∗ (0.0128696) 0.977108 ∗∗∗ (0.0080205) 0.982202 ∗∗∗ (0.0059423) 0.955039 ∗∗∗ (0.0069875) 0.971471 ∗∗∗ (0.0044284) 0.983807 ∗∗∗ (0.0029170) 0.952879 ∗∗∗ (0.0129126) 0.961144 ∗∗∗ (0.0115554) 0.983763 ∗∗∗ (0.00977724) p-value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ER T 0.6 T 0.7 T 0.8 1.07157 ∗∗∗ (0.0811107) 1.02359 ∗∗∗ (0.0597614) 0.991256 ∗∗∗ (0.0440225) 1.01662 ∗∗∗ (0.0811107) 1.04097 ∗∗∗ (0.0597614) 0.995434 ∗∗∗ (0.0440225) 0.942475 ∗∗∗ (0.0811107) 0.983888 ∗∗∗ (0.0597614) 1.03384 ∗∗∗ (0.0440225) 1.04953 ∗∗∗ (0.0342341) 1.01956 ∗∗∗ (0.0215513) 1.0159 ∗∗∗ (0.0141435) 1.02304 ∗∗∗ (0.0557715) 1.0337 ∗∗∗ (0.0319747) 1.01637 ∗∗∗ (0.0212634) 0.837879 ∗∗∗ (0.0893106) 0.960915 ∗∗∗ (0.0702213) 1.07098 ∗∗∗ (0.0562412) p-value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Note: total sample T is 436 and the three period-gram points, T 0.6 , T 0.7 and T 0.8 are 38, 70 and 129, respectively, Asterisks *** indicate 1% level of significance. Figures in square brackets represent the standard errors. Source: Computed by The authors 5.3. Fractional cointegration model estimation 5.3.1. Lag-order selection According to Table No. 04, the lowest value of the AIC information criteria suggests that a lag length of 1 in the case of Algeria, 2 in the case of Tunisia, and 4 in the case of Morocco may be a suitable choice. Considering the LR statistic and its p-value, which indicate the significance of Γ1 by rejecting the null hypothesis of Γ1 = 0 at a 1% significance level, and the fractional cointegration order 𝑏 greater than 1/2 in the all cases, all these criteria indicate that Lag 1, Lag2, Lag4 is the appropriate choice for the model for Algeria, Tunisia and Morocco, respectively. Table 4: Lag Selection Results Note: Asterisk * indicate 10% level of significance, suppose: d = b Source: Computed by The authors 5.3.2. Cointegration rank selection Table No. 05 presents the relevant test results for selecting the appropriate order of fractional cointegration. It includes the probability ratio test statistics for a specific joint integration order against an unrestricted model with full integration order when available. The p-values are calculated using the "fracdist" package, which obtains simulation-based p-values from (MacKinnon & Nielsen, 2014 ). By reading the table from the lowest order to the highest order For all three cases (Algeria, Tunisia, Morocco) we reject the null hypothesis of order 0 against order 2 because the LR statistic is higher than the critical value at all traditional significance levels. We then test the null hypothesis of order 1 against order 2, and since the LR statistic for order 1 is smaller than the critical value at all traditional significance levels, we accept the null hypothesis with a p-value of 0.692, 0.552, and 0.296, respectively. Therefore, the order of fractional cointegration is equal to 1 For all three cases (Algeria, Tunisia, Morocco), which means there is one cointegrated long-run equilibrium relationship between the consumer prices and the exchange rate in the Algeria, Tunisia and Morocco. Table 5: Cointegration Rank Results Countries Rank d b Log-likelihood LR statistic P-value ALG 0 0.563 0.563 -1251.699 71.336 0.000 1 1.171 1.171 -1217.394 2.727 0.692 2 1.180 1.180 -1216.031 ---- ---- TUN 0 0.670 0.670 991.770 7.449 0.456 1 0.635 0.635 994.731 1.526 0.552 2 0.643 0.643 995.494 ---- ---- MOR 0 0.677 0.677 -118.419 18.957 0.016 1 0.308 0.308 -109.486 1.091 0.296 2 0.316 0.316 -108.940 ---- ---- Note: ---- The LR p-values in the last column are missing Source: Computed by The authors 5.3.3. Model estimation FCVAR Through the results of Table No. 06, we observe in the case of Algeria that the fractional cointegration coefficient, d, is estimated at 1.171 It is greater than 1 ( d > 1) which means that the variance is not fixed, as exchange rate shocks cause permanent inflation persistence in the Algeria. As for Tunisia, we observe that the fractional cointegration coefficient, d, is estimated at 0.635 and is bounded between 0.50 and 1 (0.5 < d < 1). This means that the Cointegration process is highly stable but exhibits long memory with a high level of persistence in the cointegration. Hence, the variation in the inflation rate in Tunisia will persist for a longer period due to the shock of exchange rates before eventually is fading away. As for Morocco, we observe that the fractional cointegration coefficient, d, is estimated at 0.308 and is bounded between 0 and 0.50 (0 < d < 0.50). This means the cointegration process is also stationary, but it shows a long memory with low persistence in the cointegration this indicates that the effect of the exchange rate shock on the inflation rate persistence in Morocco will last for a short period. In other words, the change in the persistence of inflation rate in Morocco due to an exchange rate shock will fade away within a short period. 5.3.4. Testing the model residuals for serial correlation The results of the white noise tests are shown below. For each residual, both the Q- and LM-test statistics and their P values are reported, in addition to the multivariate Q-test and associated P value in the table No. 7, From the output of this table we can conclude that there does not appear to be any problems with serial correlation in the residuals In all There cases (Algeria, Tunisia, Morocco). Table 6: Fractional cointegration test results (FCVAR) Note: Standard errors in parentheses, Source: Computed by The authors Table 7 White Noise Test Results Countries Variable Multivar CPI ER ALG Q P-val 108.987 0.000 42.761 0.000 21.638 0.042 LM P-val ---- ---- 37.021 0.000 19.884 0.069 TUN Q P-val 44.919 0.600 14.357 0.279 20.184 0.064 LM P-val ---- ---- 12.526 0.404 15.033 0.240 MOR Q P-val 41.942 0.718 13.284 0.349 5.278 0.948 LM P-val ---- ---- 14.240 0.286 5.109 0.954 Note: ---- The LM p-values in the last column are missing Source: Computed by The authors via 5.3.5. Comparison of the FCVAR and VAR model using the LR likelihood ratio Here, we test the CVAR model (null hypothesis: d = b = 1) against the FCVAR model (alternative hypothesis: d = b ≠ 1), which restricts b = d = 1, where we reject the null hypothesis if the probability ratio (LR) is statistically significant, where we prefer the FCVAR model, otherwise the opposite, we prefer the CVAR model. By examining the test results shown in Table No. 8, which presents the log-likelihood values for both models, degrees of freedom, the LR test statistic, and the p-values estimated to be 0.000 in the all three cases (Algeria, Tunisia, Morocco), which is significant at all traditional confidence levels. Therefore, the test clearly no accepts the null hypothesis that the preferred model is CVAR. Consequently, we accept the alternative hypothesis, indicating that the FCVAR model is the better choice in the all three cases (Algeria, Tunisia, and Morocco). Table 8 LR likelihood ratio test results between the CVAR and FCVAR models Countries Unrestricted log-likelihood Restricted log-likelihood LR statistic P-value ALG -1217.394 -1228.660 22.531 0.000*** TUN 994.731 975.797 37.867 0.000*** MOR -109.486 -129.279 39.587 0.000*** Note: Asterisks *** indicate level of significance. Source: Computed by The authors via 6. CONCLUSIONS This study investigated the integration relationship between the inflation rate persistence and the exchange rates in the North African countries (Algeria, Tunisia, Morocco), analyzing the persistence of the impact of exchange rate shocks on the inflation rate persistence in the North African countries. The study contributes to the literature analyzing the degrees and determinants of inflation persistence in the North African countries by examining the effect of the external factor represented by exchange rates on inflation persistence. Given the nature of the multivariate analysis, we employed the Fractionally Cointegrated Vector Auto-regression (FCVAR) model. The results of the study are as follow: The preliminary analysis of inflation rate data in Algeria, Tunisia and Morocco indicates the inflation rate in North African countries is higher and more volatile. Confirmed that it is characterized by high inflation rates, especially during periods of economic crises, and that exchange rates in Algeria are higher and more volatile compared to the exchange rate dynamics of Morocco and Tunisia This latter is the lowest in the exchange rate. The results of the fractional integration test applied to exchange rate data on the one hand and consumer prices on the other hand, in Algeria, Tunisia and Morocco showed that it is a fractional integration. Our results also showed that Algeria, Tunisia and Morocco has a co-integration relationship between exchange rates and the inflation rate, where the persistence rate was estimated in Algeria of 1.171, it is greater than 1, which means that the variance is not fixed, as exchange rate shocks cause permanent inflation persistence in the Algeria. And the persistence rate was estimated in Tunisia of 0.635, and is bounded between 0.50 and 1, This means that the Cointegration process is highly stable but exhibits long memory with a high level of persistence in the cointegration. Hence, the variation in the inflation rate in Tunisia will persist for a longer period due to the shock of exchange rates before eventually is fading away. And the persistence rate was estimated in Morocco of 0.308, and is bounded between 0 and 0.50, this means The cointegration process is also stationary but it shows a long memory with low persistence in the cointegration This indicates that the effect of the exchange rate shock on the inflation rate persistence in a Morocco will last for a short period. In other words, the change in the persistence of inflation rate in Morocco due to an exchange rate shock will fade away within a short period. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4308266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294275512,"identity":"571c0243-615f-4cc7-a594-7b8de7771224","order_by":0,"name":"Rachid BENKHELOUF","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-9535-7237","institution":"University Centre of Maghnia","correspondingAuthor":true,"prefix":"","firstName":"Rachid","middleName":"","lastName":"BENKHELOUF","suffix":""},{"id":294275513,"identity":"2354a055-bb4a-4ba4-9544-00f865748b38","order_by":1,"name":"Abdelkader SAHED","email":"","orcid":"https://orcid.org/0000-0003-2509-5707","institution":"University Centre of Maghnia","correspondingAuthor":false,"prefix":"","firstName":"Abdelkader","middleName":"","lastName":"SAHED","suffix":""}],"badges":[],"createdAt":"2024-04-22 22:10:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4308266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4308266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55214708,"identity":"ba21f5b0-5e45-4e51-b535-2281f8254c60","added_by":"auto","created_at":"2024-04-24 07:19:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135950,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in exchange rates and consumer prices for North African countries\u003c/p\u003e\n\u003cp\u003eSource: Computed by The authors\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4308266/v1/2c4b79787a277cf6727be5a6.jpg"},{"id":55215271,"identity":"5f6d2e3d-cb9f-4cf2-94f1-27c18d012915","added_by":"auto","created_at":"2024-04-24 07:28:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":539908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4308266/v1/229547ba-0e51-404c-a340-bf3974c88f7c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExchange prices shocks and inflation rate persistence for north African countries: a fractional cointegration relationship\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eInflation is considered a macroeconomic problem for all countries around the world, without exception, due to its negative effects on economic expansion and income redistribution. Therefore, achieving an acceptable inflation rate is one of the economic goals of any central bank in any country. This task requires a deep understanding of inflation dynamics such as the inflation rate (Tule et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding the dynamics of inflation persistence will help central bank officials to make appropriate monetary policy decisions (Amano, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Coenen, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tetlow, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInflation persistence is defined as the time it takes for the shocks to inflation to dissipate (Sbordone, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and it can also be defined as the speed at which inflation returns to its equilibrium level (medium and long term) after a shock (Bilici \u0026amp; \u0026Ccedil;ekin, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The effectiveness of monetary policy strategy is determined by its ability to achieve a low level of inflation persistence, indicating that shocks to inflation are eliminated within a short period (Bratsiotis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gerlach \u0026amp; Tillmann, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meller \u0026amp; Nautz, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(Batini \u0026amp; Nelson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) Inflation persistence is sometimes defined as the tendency for price shocks to push inflation away from its stable state, including the inflation target, for an extended period. Persistence is important because it affects the costs of production in reducing inflation to the target. It is often described as a \"sacrifice ratio,\" where a lower level of persistence implies a larger policy space. Policy space refers to the ability of monetary policy to absorb temporary price shocks. Countries with high persistence and low policy space may need to adjust macroeconomic policies materially to accommodate price shocks, as they impact overall inflation and inflation expectations over a sustained period (Roache, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effectiveness of monetary policy strategy lies in its ability to achieve low inflation persistence which means that inflation rate shocks can be quickly eliminated (Bratsiotis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gerlach \u0026amp; Tillmann, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meller \u0026amp; Nautz, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Misati et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). On the contrary, the wrong appreciation of the inflation rate persistence can be charged with monetary policy makers.\u003c/p\u003e \u003cp\u003e(Batini \u0026amp; Nelson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) distinguished between three types of persistence: Positive serial correlation in inflation This refers to the positive relationship between current and lagged inflation rates, indicating a persistent pattern of inflationary movements; Time lags in the effects of systematic monetary policy measures on inflation (peak effects) This refers to the delays between the implementation of systematic monetary policy actions and their impact on inflation It highlights the lagged response of inflation to policy measures; Delayed responses of inflation to non-systematic policy measures (such as policy shocks) This type of persistence refers to the delayed effects of non-systematic policy actions on inflation. It captures the delayed response of inflation to unexpected policy shocks. In addition, there is another type of persistence mentioned by (Fuhrer \u0026amp; Moore, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), which is the inflation response to its own shocks. This type of persistence emphasises how inflation responds to its own disturbances.\u003c/p\u003e \u003cp\u003eExchange rate fluctuations are an important driver of inflation and therefore could have significant implications for determining the nature of monetary policy (Fischer, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Forbes, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mishkin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrice shocks, including exchange rate shocks, are considered external shocks that impact inflation rates in any country. Numerous studies have been conducted on the determinants of inflation persistence, but little attention has been given to the role of exchange rates as a key indicator of inflation.\u003c/p\u003e \u003cp\u003eThe expected impact of currency movements on consumer prices will determine how the central bank responds. In particular, monetary authorities may look beyond this but may choose to respond if the impact on inflation is persistent (Ball \u0026amp; Reyes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Calvo \u0026amp; Reinhart, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, an increase in the country's persistent inflation rate as a result of an exchange rate shock indicates a weakness in the effectiveness of its monetary policy. This calls for a review of the monetary policy in response to the exchange rate shock. On the other hand, if the inflation rate continues to decline or remain unchanged due to an exchange rate shock. It means that the current monetary policy of the country is responding to the exchange rate shock and there is no need to review the monetary policy in the face of such shock.\u003c/p\u003e \u003cp\u003eMonetary policy responding to price shocks in a more accommodative manner is likely to produce more persistent inflation. For that reason, failure to accommodate inflation shocks is frequently perceived as a precondition for lower inflation persistence (Alogoskoufis \u0026amp; Smith, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe importance of studying inflation persistence lies in its significant role in shaping monetary policy. It determines the extent to which monetary authorities can maintain a stable level of output and inflation simultaneously, thus influencing the performance of monetary policies (Antonakakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main objective of our study is to investigate the impact of exchange rate shocks on the inflation rate persistence in North African countries (Algeria, Tunisia and Morocco). Therefore, this study is highly important for the monetary authorities in these countries to determine whether they should review their monetary policy in the face of global exchange rate shocks. We will use the Fractional Cointegration Vector Autoregressive (FCVAR) model, introduced by (S\u0026oslash;. Johansen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and further developed by (S. Johansen \u0026amp; Nielsen, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), instead of the traditional Cointegrated Vector Autoregressive (CVAR) model proposed by (S. Johansen, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Both models capture the long-term relationship between variables, but the CVAR model assumes only two cases of long-term relationship indicated by zero-order of integration I(0) or first-order of integration I(1), while the FCVAR model allows for different orders of integration (I(d)), where d represents any real-valued order of integration, enabling long memory persistence (1\u0026thinsp;\u0026lt;\u0026thinsp;d\u0026thinsp;\u0026lt;\u0026thinsp;0) (L. A. Gil-Alana et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; L. Gil-Alana \u0026amp; Carcel, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Granville \u0026amp; Zeng, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor more details, we chose the FCVAR model instead of the CVAR model because several studies have found that the inflation rate is fractional integrated see for example: (Bilici \u0026amp; \u0026Ccedil;ekin, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Granville \u0026amp; Zeng, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tule et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The same applies to exchange rate. This makes the FCVAR approach more suitable for analyzing the long-term relationship between exchange rates and inflation.\u003c/p\u003e \u003cp\u003eThis study will contribute to the empirical literature analyzing the determinants of inflation persistence in North African countries (Algeria, Tunisia and Morocco) by examining the impact of an external factor represented by exchange rates using the recently developed Fractional Cointegration Vector Autoregressive (FCVAR) model. It will also shed light on the role of the North African country's monetary policy in modeling this impact.\u003c/p\u003e \u003cp\u003eThe organization of this study after this introductory section is as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the existing literature and evaluates its findings and highlights the added value of our study, Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the data and its temporal evolution, Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e outlines the methodological framework of the study, Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results and discussion and Finally, Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes the paper.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe inflation persistence is the time it takes for inflation rate shocks to dissipate (Sbordone, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and it can also be defined as the speed at which inflation rate returns to its long-term equilibrium level after a shock (Bilici \u0026amp; \u0026Ccedil;ekin, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding the nature of inflation persistence will assist authorities and policy makers in implementing effective monetary policies towards maintaining price stability and economic stability (Bernanke et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Numerous studies have been conducted in this field, which can be categorized into three types. Firstly studies that believe inflation persistence dynamics are determined by the characteristics of the inflation rate; These studies employ techniques such as single-variable autoregressive modeling, including tests for stationary, partial integration, fractional unit roots, or time-varying parameter estimation. There are consistent findings of these studies, for example The study by (Bilici \u0026amp; \u0026Ccedil;ekin, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used a time-varying parameter (TVP) estimation model to examine inflation persistence in Turkey. They found that inflation persistence increases and exhibits high volatility during periods of high inflation. (Bratsiotis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) study explored the role of the inflation target in inflation persistence in seven countries. Their findings revealed that inflation targets significantly reduce inflation persistence. (Darvas \u0026amp; Varga, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) study in which they analyzed the dynamics of inflation persistence in Central and Eastern European countries and found that inflation persistence tends to be higher in times of high inflation. (Gerlach \u0026amp; Tillmann, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) study in which they analyzed the persistence of inflation in Asia-Pacific countries before and after the introduction of inflation targeting. They found that the speed at which persistence declines varies from one country to another and that persistence tends to decline after the adoption of inflation targeting.\u003c/p\u003e \u003cp\u003e(Granville \u0026amp; Zeng, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Secondly, some studies give an important role to inflation expectations in determining the inflation persistence, where the continuity of inflation based on expectations is driven by differences between public perceptions about the inflation target and the real inflation target (explicit or implicit) set by the central bank. Simple and brief auto-regression analyzes are used to predict inflation and this type. One of the models makes the degree of perceived and actual inflation persistence varies over time and a function of the history of shocks, For example we mention the study of (Erceg \u0026amp; Levin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) which shows how identification of the inflation target set by the central bank can explain the process of gradual deflation during the Volcker era in a purely forward-looking model. Likewise, When private agents have incomplete information about whether a given disturbance in inflation is due to a temporary or permanent supply shock, inflation expectations may adjust only gradually after a purely temporary shock, making the inflationary response more persistent. A study by (Orphanides \u0026amp; Williams, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) in which they explain that active central banks that are very concerned with stabilizing the output gap may slow down the learning process of agents trying to predict inflation and thus may increase the continuity of the inflation process. In this case, the monetary policy system will affect the formation of inflation expectations and may effect on the continuity of inflation through this channel. In general, the presence of a credible political system that focuses on price stability would reduce the continuity of inflation. The study of (Gaspar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) that found a clear map between the monetary policy system and the distribution of the stability parameter in the perceived law of motion of inflation such that the continuous responses to cost-increasing shocks and the stability of inflation expectations resemble the ideal policy under commitment and rational expectations and in this case it depends on the expectations of policy actions. Impacting future inflation expectations. (Granville \u0026amp; Zeng, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) investigated the dynamics of inflation persistence in the United States and concluded that the dynamics of inflation persistence are related to expectations shaped by memories of past inflation. (Zhang, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) study investigated the relationship between inflation persistence, inflation expectations and monetary policy in China. The study suggests that the dynamics of inflation expectations can be related to structural change in inflation, this finding implies that the quiescence of inflation in China over the past decade could well be followed by a return to a high inflation era in the absence of a determined effort by the monetary authorities in managing inflation expectations. Therefore, the use of a preemptive monetary policy to anchor inflationary expectations and to keep inflation moderate is warranted in China. (Antonakakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) study that the degree of inflation persistence from the estimated long memory parameter is relatively small when considering online price indices as a measure of inflation and this indicates a higher level of efficiency of monetary policies in managing the prices.\u003c/p\u003e \u003cp\u003eThird other studies suggest that inflation persistence can be influenced by structural economic factors such as progressive taxation, human capital and the monetary policy framework, particularly inflation targeting or exchange rate regimes. These studies have yielded different results, for example The study by (Oloko et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) aimed to analyze the relationship between the gold price and inflation rate using the fractional cointegration model to determine the extent of inflation persistence in the face of gold price shocks in some advanced and developing countries. The results indicated that the impact of gold price shocks remains significant for a long period in relation to inflation persistence in developing countries and for a short period in relation to inflation persistence in advanced countries. The study by (Oloko et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) investigated the effect of oil price shocks on inflation persistence among the top ten oil-exporting and oil-importing countries using the fractional cointegration VAR model. The results showed that the inflation persistence in both oil-exporting and oil-importing countries is not significantly increased due to oil price shocks, indicating that the monetary policies of these countries accommodate oil price shocks. A study (Geronikolaou et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) that investigated the effect of progressive taxes and human capital on the persistence of inflation in 28 OECD countries, where they found that increasing progressive taxes reduces the spread of shocks and thus increases the persistence of inflation. The dispersion of human capital across sectors acts as a barrier to labor mobility and thus further inflation rigidity through the same channel. The study of (Wu \u0026amp; Wu, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examined the role of the flexible exchange rate system in continuing inflation using 23 industrialized countries, and its results concluded that there is ambiguity in the effect of the exchange rate system on the continuity of relative inflation; Floating compared to bond rates. A Study (Canarella \u0026amp; Miller, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who analyzed the relationship between inflation targeting and inflation persistence in selected developed countries (Canada, Sweden, United Kingdom) and industrialized and newly emerging economies (Chile, Israel, and Mexico) that adopted inflation targeting (information technology) before 2000. They concluded Overall results were mixed and varied according to the level of development in countries. Specifically, The inflationary processes in the three advanced economies were partially integrated, steady, medium-yielding, and share a common inflationary persistence. Whereas, the inflationary processes in the three emerging market economies were partially integrated, medium return, and unstable. The study conducted by (Salisu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found mixed short-term and similar long-term relationships between oil prices and inflation rates in both oil-importing and oil-exporting countries. This indicates that the empirical result of the impact of oil price shocks on inflation rate persistence in oil-importing and oil-exporting countries may be similar or divergent. Study by (Kilian \u0026amp; Park, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), This study investigated the role of oil price shocks in inflation persistence in a group of industrialized countries. It found that oil price shocks have a substantial impact on inflation persistence, with the effect varying across countries. The study by (Mishkin \u0026amp; Schmidt-Hebbel, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) examined the impact of the monetary policy framework on inflation persistence and showed that medium-term inflation targeting reduces inflation persistence more than fixed-money targeting. A Study by (Burdekin \u0026amp; Siklos, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), Using long time series data from the United Kingdom, the United States, Canada, and Sweden, we suggest that these authors' emphasis on a post-1967 shift in inflation persistence is misplaced and that there are other equally good candidates to account for changes in inflation persistence such as wars, oil price shocks, and central bank reforms. A Study by (Bleaney \u0026amp; Francisco, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) Using data for 102 developing countries, it is shown that inflation persistence is particularly low in countries on hard pegs, and particularly high in countries with severe inflationary problems. Inflation persistence is similar under floating and soft pegs. A study by (Giannellis \u0026amp; Koukouritakis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) tests the conjecture that inflation is persistent in selected countries in Latin America and, by establishing an appropriate nonlinear model, finds no strong evidence in favor of the above generally accepted view. However, the available evidence shows that in periods of significant decline in the value of the domestic currency, the domestic inflation rate was persistent, whereas in periods of slower decline, or relative stability, it was temporary.\u003c/p\u003e \u003cp\u003eThis study falls within the category of studies that consider structural factors as additional determinants of inflation rate persistence. It focuses on an external factor, which is exchange rates, in contrast to the local factors addressed in previous studies. Moreover, it focuses specifically on North African countries (Algeria, Tunisia, and Morocco), as no study on the inflation rate persistence has yet been conducted in these countries, especially given their divergent economic characteristics. Additionally, the study utilizes the Fractional Co-integration model as a new and developed standard approach. Lastly, to the best of our knowledge, this article is one of the initial attempts to study the relationship between exchange rates and inflation rate persistence in North African countries, which serves as a motivation for undertaking this study.\u003c/p\u003e"},{"header":"3. Data","content":"\u003cp\u003eIn this study, we use monthly data for each of the exchange rate of North African countries (Algeria, Tunisia and Morocco) measured in the currency of each country in relation to the US dollar, and the inflation rate expressed in consumer prices measured in the currency of each country from January 1987 to Avril 2023. This dataset comprises 436 observations, covering periods of both high and low inflation rates in North African countries, as well as positive and negative exchange rate shocks, We represent an exchange rate as \"ER\" and consumer prices as \"CPI.\", We represent the countries of Algeria, Tunisia, and Morocco as \"ALG,\" \"TUN,\" and \"MOR,\" respectively. All data were obtained from the World Bank website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://databank.worldbank.org/\u003c/span\u003e\u003cspan address=\"https://databank.worldbank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCPI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eALG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTUN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMOR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.34451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.00166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.03239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.16928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.525530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.099816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.32727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.70202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.62810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.42811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.321404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.946609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206.8364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205.5556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.0221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146.4752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.270952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.87833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eminimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.15458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.99495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.19254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.857635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.786095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.290864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.47340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.86194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.58851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.33886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.661314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.870491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.16687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.94254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.73867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.777720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.09545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.83012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebropability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: Computed by The authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;1 presents the trends in exchange rate and consumer prices for North African countries. From the figure, some degree of co-movement between observed consumer prices and exchange rate can be observed, which suggests the presence of cointegration. More Algeria and Tunisia appear to exhibit a higher degree of co-movement than Morocco. This suggests that these countries (Algeria and Tunisia) may exhibit higher exchange rate-inflation cointegrating persistence than Morocco.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics for exchange rate and consumer prices for the North African countries. The table shows that the average consumer prices and standard deviation for Algeria is 88.34 percent and 50.47 percent, respectively; And Tunisia is 94 percent and 40.86 percent, respectively, and Morocco is 90.03 percent and 19.58 percent, respectively. This suggests that inflation rate in North African countries is higher and more volatile.\u003c/p\u003e \u003cp\u003eThe table also shows that the average exchange rate and standard deviation for Algeria is 69.16 percent and 37.33 percent, respectively; and Tunisia is 1.32 percent and 0.66 percent, respectively, and Morocco is 8.94 percent and 0.87 percent, respectively. This suggests that the exchange rate in Algeria is higher and more volatile compared to the exchange rate dynamics of Morocco and Tunisia This latter is the lowest in exchange rate.\u003c/p\u003e"},{"header":"4. METHODS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Fractional integration approaches\u003c/h2\u003e \u003cp\u003eHere is a brief description of the method that will be adopted in this study, which is the recently developed Fractional Cointegration Vector Auto-regression (FCVAR) model by (S. Johansen \u0026amp; Nielsen, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This model has gained significant popularity, as evident from its recent application in various fields of study, including (Solarin Sakiru Adebola et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), (Aye et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), (L. A. Gil-Alana et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), (L. Gil-Alana \u0026amp; Carcel, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Nielsen \u0026amp; Shibaev, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), (Tule et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and (Yaya et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), This model allows for fractional integration with non-zero orders when the series is not integrated at the zero order (L. Gil-Alana \u0026amp; Carcel, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The latter provides researchers with evidence of when transience is found to be reasonable, rather than assuming the explicit nature of permanent shocks that require a long time to fade away. Therefore, we will apply the framework of fractional integration to consumer prices in the North African countries and exchange rates.\u003c/p\u003e \u003cp\u003eTo explain fractional integration, we define the typical process I(d) in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) below.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$(1-L{)}^{d}{x}_{t}={u}_{t},t=0,\\pm 1,\\dots$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere d can be any real value, L is the lag-operator (Lxt\u0026thinsp;=\u0026thinsp;xt-1) and ut is I(0), defined as a covariance stationary process with a spectral density function that is positive and finite at the zero frequency. The idea of fractional integration was introduced by (C. W. Granger, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; C. W. J. Granger, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; C. W. Granger \u0026amp; Joyeux, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Hosking, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), although (Adenstedt, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) had already given evidence of its meaning. The polynomial (1) Ld in (1) can be formulated in terms of its binomial expansion in such a way that for all real d,\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$(1-L{)}^{d}=\\sum _{j=0}^{\\infty } {\\psi }_{j}{L}^{j}=\\sum _{j=0}^{\\infty } \\left(\\begin{array}{c}d\\\\ j\\end{array}\\right)(-1{)}^{j}{L}^{j}=1-dL+\\frac{d(d-1)}{2}{L}^{2}-\\dots ,$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAnd thus:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$(1-L{)}^{d}{x}_{t}={x}_{t}-d{x}_{t-1}+\\frac{d(d-1)}{2}{x}_{t-2}-\\dots$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eImplying that Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) can be expressed as\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${x}_{t}=d{x}_{t-1}-\\frac{d(d-1)}{2}{x}_{t-2}+\\dots +{u}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eGiven the parameterization in (1) we can distinguish different cases depending on the value of d. If d\u0026thinsp;=\u0026thinsp;0, xt\u0026thinsp;=\u0026thinsp;ut, xt is said to be \u0026ldquo;short memory\u0026rdquo; or I(0); if d\u0026thinsp;\u0026gt;\u0026thinsp;0, xt is said to be \u0026ldquo;long memory\u0026rdquo;, because of the strong association between observations which are far in time. Here, if d belongs to the interval (0, 0.5) then xt is still covariance stationary, while d\u0026thinsp;\u0026ge;\u0026thinsp;0.5 implies non-stationarity. Finally, if d\u0026thinsp;\u0026lt;\u0026thinsp;1, the series is mean reverting, meaning that the effect of shocks will eventually disappear in the long run, contrary to what happens if d\u0026thinsp;\u0026ge;\u0026thinsp;1, with shocks persisting forever (L. Gil-Alana \u0026amp; Carcel-Villanova, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The fractionally cointegration VAR model\u003c/h2\u003e \u003cp\u003eThe theoretical derivation of the fractional integration model has been shown in the previous section. As a univariate model, this includes one fractional integration parameter, d, which describes the order of integration of either consumer prices or exchange rates. As exchange rates and consumer prices are fractionally integrated, the next is to determine evidence of fractional cointegration between the two variables. In other words, This is to determine the existence of common fractional integration between exchange rates and consumer prices in the North African countries As the differencing parameter, d, an integer value, was not restricted by (Engle \u0026amp; Granger, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), (Robinson, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) introduced the fractional cointegration technique which allows for simultaneous estimation of the differencing parameter d as well as other parameters in the relationship. In a broader sense, given two real numbers d, b, the components of the vector zt are said to be cointegrated of order d, b, denoted zt \u0026sim; CI(b, d) if:\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-roman;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAll the components of zt are I(d)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThere exists a vector α ∕= 0 such that st\u0026thinsp;=\u0026thinsp;α \u0026prime;zt \u0026sim; I(γ)\u0026thinsp;=\u0026thinsp;I(d\u0026thinsp;\u0026minus;\u0026thinsp;b), b\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWhere α is the cointegrating vector and st is the error term (Aye et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the multivariate model specification we start our model specification with the CVAR model and after that the FCVAR model since the latter is a fractional modification of the first, The CVAR model is:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\varDelta {Y}_{t}=\\alpha {\\beta }^{{\\prime }}{Y}_{t-1}+\\sum _{i=1}^{k} {\\varGamma }_{i}\\varDelta {Y}_{t-i}+{\\epsilon }_{t}=\\alpha {\\beta }^{{\\prime }}L{Y}_{t}+\\sum _{i=1}^{k} {\\varGamma }_{i}\\varDelta {L}^{i}{Y}_{t}+{\\epsilon }_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe simplest way to derive the FCVAR model is to replace the difference and lag operators and L in (2) by their fractional counterparts, b and Lb\u0026thinsp;=\u0026thinsp;1 b, respectively. We then obtain\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${{\\Delta }}^{b}{Y}_{t}=\\alpha {\\beta }^{{\\prime }}{L}_{b}{Y}_{t}+\\sum _{i=1}^{k} {{\\Gamma }}_{i}{{\\Delta }}^{b}{L}_{b}^{i}{Y}_{t}+{\\epsilon }_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhich is applied to Y X t\u0026thinsp;=\u0026thinsp;d b t such that\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${{\\Delta }}^{d}{X}_{t}=\\alpha {\\beta }^{{\\prime }}{L}_{b}{{\\Delta }}^{d-b}{X}_{t}+\\sum _{i=1}^{k} {{\\Gamma }}_{i}{{\\Delta }}^{b}{L}_{b}^{i}{Y}_{t}+{\\epsilon }_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere ℇt is p-dimensional independent and identically distributed, with mean zero and covariance matrix Ω. The parameters have the usual interpretations of the CVAR model. Thus, α and β are p \u0026times; r matrices, where 0\u0026thinsp;\u0026le;\u0026thinsp;r\u0026thinsp;\u0026le;\u0026thinsp;p. The columns of β are the cointegrating relationships in the system, that is to say the long-run equilibria. The parameters Γi govern the short-run behavior of the variables and the coefficients in represent the speed of adjustment towards equilibrium for each of the variables. The FCVAR model permits simultaneous modeling of the long-run equilibria, the adjustment responses to deviations from the equilibria and the short-run dynamics of the system (L. A. Gil-Alana et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; L. Gil-Alana \u0026amp; Carcel-Villanova, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CVAR model is a special case of the FCVAR model that has this relationship, d\u0026thinsp;=\u0026thinsp;b =\u0026thinsp;1. To derive implication for inflation persistence from the fractional cointegration between inflation and exchange rate, which will be interesting to monetary authorities, the vector of endogenous variables was normalized on consumer prices in this study, such that we have:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${\\text{ }\\text{CPI}\\text{ }}_{\\text{t}}=\\alpha +\\beta {\\text{ ER }}_{\\text{t}-\\text{k}}+{x}_{t},(1-\\text{L}{)}^{\\text{d}}{x}_{t}={\\text{u}}_{\\text{t}},\\text{t}=\\text{1,2},\\dots ,$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere the parameter d indicates the degree of persistence, β is now an indicator of the effect of the present (and past) exchange rate on domestic consumer prices of the respective countries (L. A. Gil-Alana et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The differencing parameter, d, in the cointegrating equation, Eq.\u0026nbsp;(\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), relies on equality between fractional integration of consumer prices and exchange rates (dCPI\u0026thinsp;=\u0026thinsp;dER). Hence, d in Eq.\u0026nbsp;(\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) is the fractional cointegrating persistence, which can explain the effect of shocks to the exchange rate of inflation persistence.\u003c/p\u003e \u003cp\u003eThe fractional cointegration between exchange rate and the consumer prices describes how exchange rate shocks can affect the persistence of inflation in four cases depending on the values of the cointegration difference coefficient d:\u003c/p\u003e \u003cp\u003eThe first case is where (d\u0026thinsp;=\u0026thinsp;0) the cointegration process is constant and has a short memory with no cointegration continuity. This means that the change in the inflation rate persistence due to the exchange rate shock will vanish almost immediately, in other words the effect of the exchange rate shocks on the inflation rate persistence does not persist. The second case is (0\u0026thinsp;\u0026lt;\u0026thinsp;d\u0026thinsp;\u0026lt;\u0026thinsp;0.5) The cointegration process is also stationary, but it shows a long memory with low persistence in the cointegration This indicates that the effect of the exchange rate shock on the inflation rate persistence in a country will last for a short period. In other words, the change in the persistence of inflation rate in a country due to an exchange rate shock will fade away within a short period. The third case is (0.5\u0026thinsp;\u0026lt;\u0026thinsp;d\u0026thinsp;\u0026lt;\u0026thinsp;1) the cointegration process is also highly stable, but it shows a long memory with a high continuity in the cointegration, meaning that the change in the inflation rate persistence due to the shock of exchange rate will continue for a longer period before it finally fades. The fourth case is (d\u0026thinsp;\u0026ge;\u0026thinsp;1), which means that the variance is not fixed, as exchange rate shocks cause permanent inflation to continue (Aye et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; L. A. Gil-Alana et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tule et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen ensuring that each series is fractional integrated, the FCVAR model estimation is performed in four steps: First Determine the optimal delay length model; Second Determine the degree of integration; Third Partial cointegration test using specified optimal delay and cointegration order; Fourth testing the model residuals for serial correlation; And Finally A comparison between the FCVAR model and the CVAR model using the probability ratio [LR] test.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Stationarity of Data\u003c/h2\u003e\n\u003cp\u003eBy conducting Dickey-Fuller (ADF) and Philips-Perron (PP) tests, the results came as shown in Table No. 02, where we do not reject the hypothesis that there is a unit root in each of the time series of the consumer prices and the exchange rate for all countries (Algeria, Tunisia and Morocco), since the \u0026ldquo;t\u0026rdquo; statistics Greater than critical values at all levels of conventional significance. The probabilities also show that the unit root hypothesis is not rejected for the consumer prices and the exchange rate, the non-stationarity of the time series allows further tests for the cointegration.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAugmented Dickey\u0026ndash;Fuller (ADF) and Phillips\u0026ndash;Perron (PP) test results\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePhillips\u0026ndash;Perron test (PP)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAt Level\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCPI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eER\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eALG\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTUN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMOR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eALG\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTUN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMOR\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\n\u003cp\u003eWith Constant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-Statistic\u003c/p\u003e\n\u003cp\u003eProb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.759858\u003c/p\u003e\n\u003cp\u003e1.000000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.631413\u003c/p\u003e\n\u003cp\u003e1.000000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.255183\u003c/p\u003e\n\u003cp\u003e0.928397\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4199\u003c/p\u003e\n\u003cp\u003e0.9029\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1054\u003c/p\u003e\n\u003cp\u003e0.9976\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.2228\u003c/p\u003e\n\u003cp\u003e0.1985\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith Constant \u0026amp; Trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-Statistic\u003c/p\u003e\n\u003cp\u003eProb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.880763\u003c/p\u003e\n\u003cp\u003e0.999998\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.74666\u003c/p\u003e\n\u003cp\u003e1.000000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.538006\u003c/p\u003e\n\u003cp\u003e0.815153\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.4232\u003c/p\u003e\n\u003cp\u003e0.8531\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.0315\u003c/p\u003e\n\u003cp\u003e0.9373\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.3109\u003c/p\u003e\n\u003cp\u003e0.4266\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWithout Constant \u0026amp; Trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-Statistic\u003c/p\u003e\n\u003cp\u003eProb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.789520\u003c/p\u003e\n\u003cp\u003e1.000000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.69340\u003c/p\u003e\n\u003cp\u003e1.000000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.648119\u003c/p\u003e\n\u003cp\u003e1.000000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9986\u003c/p\u003e\n\u003cp\u003e0.9994\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9106\u003c/p\u003e\n\u003cp\u003e0.9992\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1244\u003c/p\u003e\n\u003cp\u003e0.7214\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAugmented Dickey-Fuller test (ADF)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAt Level\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCPI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eALG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTUN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eALG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTUN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith Constant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-Statistic\u003c/p\u003e\n\u003cp\u003eProb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5395\u003c/p\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.8651\u003c/p\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.2459\u003c/p\u003e\n\u003cp\u003e0.9297\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.5101\u003c/p\u003e\n\u003cp\u003e0.8862\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7569\u003c/p\u003e\n\u003cp\u003e0.9932\u003c/p\u003e\n\u003cp\u003en0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.6733\u003c/p\u003e\n\u003cp\u003e0.0795\u003c/p\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith Constant \u0026amp; Trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-Statistic\u003c/p\u003e\n\u003cp\u003eProb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7353\u003c/p\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.6393\u003c/p\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.4710\u003c/p\u003e\n\u003cp\u003e0.8382\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.5193\u003c/p\u003e\n\u003cp\u003e0.8218\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1524\u003c/p\u003e\n\u003cp\u003e0.9176\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.7465\u003c/p\u003e\n\u003cp\u003e0.2184\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWithout Constant \u0026amp; Trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-Statistic\u003c/p\u003e\n\u003cp\u003eProb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9983\u003c/p\u003e\n\u003cp\u003e0.9994\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3121\u003c/p\u003e\n\u003cp\u003e0.9998\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.1863\u003c/p\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7674\u003c/p\u003e\n\u003cp\u003e0.9988\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5739\u003c/p\u003e\n\u003cp\u003e0.9978\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0438\u003c/p\u003e\n\u003cp\u003e0.6963\u003c/p\u003e\n\u003cp\u003eno\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eNotes: (*) Significant at the 10% and (no) Not Significant *MacKinnon (1996) one-sided p-values.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eSource: Computed by The authors\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e5.2. Fractional integrated model estimation\u003c/h2\u003e\n\u003cp\u003eBy conducting Dickey-Fuller (ADF) and Philips-Perron (PP) tests, the results came as shown in Table No. 02, where we do not reject the hypothesis that there is a unit root in each of the time series of the consumer prices and the exchange rate for all countries (Algeria, Tunisia and Morocco), since the \u0026ldquo;t\u0026rdquo; statistics Greater than critical values at all levels of conventional significance. The probabilities also show that the unit root hypothesis is not rejected for the consumer prices and the exchange rate, the non-stationarity of the time series allows further tests for the cointegration.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" style=\"width: 1025px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFractional integration estimates based on local Whittle estimator and GPH test\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003evariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003em\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLocal Whittle Estimator\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eGPH test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTUN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTUN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMOR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCPI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT \u003csup\u003e0.6\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eT \u003csup\u003e0.7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eT \u003csup\u003e0.8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.976689 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0811107)\u003c/p\u003e\n\u003cp\u003e0.968422 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0597614)\u003c/p\u003e\n\u003cp\u003e0.94761 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0440225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.953731\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0811107)\u003c/p\u003e\n\u003cp\u003e0.962333\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0597614)\u003c/p\u003e\n\u003cp\u003e0.948778 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0440225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.949635\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0811107)\u003c/p\u003e\n\u003cp\u003e0.947989\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0597614)\u003c/p\u003e\n\u003cp\u003e0.94613 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0440225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.976783\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0128696)\u003c/p\u003e\n\u003cp\u003e0.977108 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0080205)\u003c/p\u003e\n\u003cp\u003e0.982202\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0059423)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.955039\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0069875)\u003c/p\u003e\n\u003cp\u003e0.971471\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0044284)\u003c/p\u003e\n\u003cp\u003e0.983807 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0029170)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.952879\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0129126)\u003c/p\u003e\n\u003cp\u003e0.961144\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0115554)\u003c/p\u003e\n\u003cp\u003e0.983763 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.00977724)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT \u003csup\u003e0.6\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eT \u003csup\u003e0.7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eT \u003csup\u003e0.8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07157\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0811107)\u003c/p\u003e\n\u003cp\u003e1.02359\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0597614)\u003c/p\u003e\n\u003cp\u003e0.991256 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0440225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01662\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0811107)\u003c/p\u003e\n\u003cp\u003e1.04097 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0597614)\u003c/p\u003e\n\u003cp\u003e0.995434 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0440225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.942475\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0811107)\u003c/p\u003e\n\u003cp\u003e0.983888\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0597614)\u003c/p\u003e\n\u003cp\u003e1.03384 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0440225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04953\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0342341)\u003c/p\u003e\n\u003cp\u003e1.01956\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0215513)\u003c/p\u003e\n\u003cp\u003e1.0159 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0141435)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02304\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0557715)\u003c/p\u003e\n\u003cp\u003e1.0337\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0319747)\u003c/p\u003e\n\u003cp\u003e1.01637\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0212634)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.837879\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0893106)\u003c/p\u003e\n\u003cp\u003e0.960915 \u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0702213)\u003c/p\u003e\n\u003cp\u003e1.07098\u003csup\u003e\u0026lowast;\u0026lowast;\u0026lowast;\u003c/sup\u003e (0.0562412)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eNote: total sample T is 436 and the three period-gram points, T\u003csup\u003e0.6\u003c/sup\u003e, T\u003csup\u003e0.7\u003c/sup\u003e and T\u003csup\u003e0.8\u003c/sup\u003e are 38, 70 and 129, respectively, Asterisks *** indicate 1% level of significance. Figures in square brackets represent the standard errors.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eSource: Computed by The authors\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e5.3. Fractional cointegration model estimation\u003c/h2\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.1. Lag-order selection\u003c/h2\u003e\n\u003cp\u003eAccording to Table No. 04, the lowest value of the AIC information criteria suggests that a lag length of 1 in the case of Algeria, 2 in the case of Tunisia, and 4 in the case of Morocco may be a suitable choice. Considering the LR statistic and its p-value, which indicate the significance of \u0026Gamma;1 by rejecting the null hypothesis of \u0026Gamma;1\u0026thinsp;=\u0026thinsp;0 at a 1% significance level, and the fractional cointegration order 𝑏 greater than 1/2 in the all cases, all these criteria indicate that Lag 1, Lag2, Lag4 is the appropriate choice for the model for Algeria, Tunisia and Morocco, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003eLag Selection Results\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" width=\"754\" height=\"620\" /\u003e\u003c/p\u003e\n\u003cp\u003eNote: Asterisk * indicate 10% level of significance, suppose: d = b\u003c/p\u003e\n\u003cp\u003eSource: Computed by The authors\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.2. Cointegration rank selection\u003c/h2\u003e\n\u003cp\u003eTable No. 05 presents the relevant test results for selecting the appropriate order of fractional cointegration. It includes the probability ratio test statistics for a specific joint integration order against an unrestricted model with full integration order when available. The p-values are calculated using the \"fracdist\" package, which obtains simulation-based p-values from (MacKinnon \u0026amp; Nielsen, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). By reading the table from the lowest order to the highest order For all three cases (Algeria, Tunisia, Morocco) we reject the null hypothesis of order 0 against order 2 because the LR statistic is higher than the critical value at all traditional significance levels. We then test the null hypothesis of order 1 against order 2, and since the LR statistic for order 1 is smaller than the critical value at all traditional significance levels, we accept the null hypothesis with a p-value of 0.692, 0.552, and 0.296, respectively. Therefore, the order of fractional cointegration is equal to 1 For all three cases (Algeria, Tunisia, Morocco), which means there is one cointegrated long-run equilibrium relationship between the consumer prices and the exchange rate in the Algeria, Tunisia and Morocco.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eCointegration Rank Results\u003c/p\u003e\n\u003ctable style=\"width: 606.562px;\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 73px;\"\u003e\n\u003cp\u003eCountries\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 526.562px;\"\u003e\n\u003cp\u003eRank \u0026nbsp; \u0026nbsp; \u0026nbsp; d \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; b \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Log-likelihood \u0026nbsp; \u0026nbsp; \u0026nbsp;LR statistic \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;P-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 73px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eALG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 526.562px;\"\u003e\n\u003cp\u003e0 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.563 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.563 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -1251.699 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 71.336 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.171 \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.171 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-1217.394 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003e2.727 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.692\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.180 \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.180 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -1216.031 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;---- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;----\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 73px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eTUN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 526.562px;\"\u003e\n\u003cp\u003e0 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.670 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.670 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 991.770 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;7.449 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.456\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.635 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.635 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 994.731 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003e1.526 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.552\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.643 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.643 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 995.494 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ---- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ----\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 73px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eMOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 526.562px;\"\u003e\n\u003cp\u003e0 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.677 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.677 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -118.419 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;18.957 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.016\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.308 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.308 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -109.486 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003e1.091 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.296\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.316 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.316 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -108.940 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ---- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;----\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: ---- The LR p-values in the last column are missing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSource: Computed by The authors\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.3. Model estimation FCVAR\u003c/h2\u003e\n\u003cp\u003eThrough the results of Table No. 06, we observe in the case of Algeria that the fractional cointegration coefficient, d, is estimated at 1.171 It is greater than 1 ( d\u0026thinsp;\u0026gt;\u0026thinsp;1) which means that the variance is not fixed, as exchange rate shocks cause permanent inflation persistence in the Algeria.\u003c/p\u003e\n\u003cp\u003eAs for Tunisia, we observe that the fractional cointegration coefficient, d, is estimated at 0.635 and is bounded between 0.50 and 1 (0.5\u0026thinsp;\u0026lt;\u0026thinsp;d\u0026thinsp;\u0026lt;\u0026thinsp;1). This means that the Cointegration process is highly stable but exhibits long memory with a high level of persistence in the cointegration. Hence, the variation in the inflation rate in Tunisia will persist for a longer period due to the shock of exchange rates before eventually is fading away.\u003c/p\u003e\n\u003cp\u003eAs for Morocco, we observe that the fractional cointegration coefficient, d, is estimated at 0.308 and is bounded between 0 and 0.50 (0\u0026thinsp;\u0026lt;\u0026thinsp;d\u0026thinsp;\u0026lt;\u0026thinsp;0.50). This means the cointegration process is also stationary, but it shows a long memory with low persistence in the cointegration this indicates that the effect of the exchange rate shock on the inflation rate persistence in Morocco will last for a short period. In other words, the change in the persistence of inflation rate in Morocco due to an exchange rate shock will fade away within a short period.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.4. Testing the model residuals for serial correlation\u003c/h2\u003e\n\u003cp\u003eThe results of the white noise tests are shown below. For each residual, both the Q- and LM-test statistics and their P values are reported, in addition to the multivariate Q-test and associated P value in the table No. 7, From the output of this table we can conclude that there does not appear to be any problems with serial correlation in the residuals In all There cases (Algeria, Tunisia, Morocco).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u0026nbsp;\u003c/strong\u003eFractional cointegration test results (FCVAR)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" width=\"740\" height=\"1056\" /\u003e\u003c/p\u003e\n\u003cp\u003eNote: Standard errors in parentheses, Source: Computed by The authors\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eWhite Noise Test Results\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountries\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMultivar\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCPI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eER\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eALG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ\u003c/p\u003e\n\u003cp\u003eP-val\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108.987\u003c/p\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.761\u003c/p\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.638\u003c/p\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLM\u003c/p\u003e\n\u003cp\u003eP-val\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.021\u003c/p\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.884\u003c/p\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTUN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ\u003c/p\u003e\n\u003cp\u003eP-val\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.919\u003c/p\u003e\n\u003cp\u003e0.600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.357\u003c/p\u003e\n\u003cp\u003e0.279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.184\u003c/p\u003e\n\u003cp\u003e0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLM\u003c/p\u003e\n\u003cp\u003eP-val\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.526\u003c/p\u003e\n\u003cp\u003e0.404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.033\u003c/p\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ\u003c/p\u003e\n\u003cp\u003eP-val\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.942\u003c/p\u003e\n\u003cp\u003e0.718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.284\u003c/p\u003e\n\u003cp\u003e0.349\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.278\u003c/p\u003e\n\u003cp\u003e0.948\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLM\u003c/p\u003e\n\u003cp\u003eP-val\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.240\u003c/p\u003e\n\u003cp\u003e0.286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.109\u003c/p\u003e\n\u003cp\u003e0.954\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: ---- The LM p-values in the last column are missing\u003c/em\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eSource: Computed by The authors via\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.5. Comparison of the FCVAR and VAR model using the LR likelihood ratio\u003c/h2\u003e\n\u003cp\u003eHere, we test the CVAR model (null hypothesis: d\u0026thinsp;=\u0026thinsp;b\u0026thinsp;=\u0026thinsp;1) against the FCVAR model (alternative hypothesis: d\u0026thinsp;=\u0026thinsp;b\u0026thinsp;\u0026ne;\u0026thinsp;1), which restricts b\u0026thinsp;=\u0026thinsp;d\u0026thinsp;=\u0026thinsp;1, where we reject the null hypothesis if the probability ratio (LR) is statistically significant, where we prefer the FCVAR model, otherwise the opposite, we prefer the CVAR model. By examining the test results shown in Table No. 8, which presents the log-likelihood values for both models, degrees of freedom, the LR test statistic, and the p-values estimated to be 0.000 in the all three cases (Algeria, Tunisia, Morocco), which is significant at all traditional confidence levels. Therefore, the test clearly no accepts the null hypothesis that the preferred model is CVAR. Consequently, we accept the alternative hypothesis, indicating that the FCVAR model is the better choice in the all three cases (Algeria, Tunisia, and Morocco).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLR likelihood ratio test results between the CVAR and FCVAR models\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountries\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnrestricted log-likelihood\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRestricted log-likelihood\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLR statistic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-value\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\n\u003cp\u003e\u003cstrong\u003eALG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-1217.394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-1228.660\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e22.531\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTUN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e994.731\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e975.797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e37.867\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-109.486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e-129.279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e\u003cstrong\u003e39.587\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\"\u003e\n\u003cp\u003e0.000***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eNote: Asterisks *** indicate level of significance.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eSource: Computed by The authors via\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"6. CONCLUSIONS","content":"\u003cp\u003eThis study investigated the integration relationship between the inflation rate persistence and the exchange rates in the North African countries (Algeria, Tunisia, Morocco), analyzing the persistence of the impact of exchange rate shocks on the inflation rate persistence in the North African countries. The study contributes to the literature analyzing the degrees and determinants of inflation persistence in the North African countries by examining the effect of the external factor represented by exchange rates on inflation persistence. Given the nature of the multivariate analysis, we employed the Fractionally Cointegrated Vector Auto-regression (FCVAR) model. The results of the study are as follow:\u003c/p\u003e \u003cp\u003eThe preliminary analysis of inflation rate data in Algeria, Tunisia and Morocco indicates the inflation rate in North African countries is higher and more volatile. Confirmed that it is characterized by high inflation rates, especially during periods of economic crises, and that exchange rates in Algeria are higher and more volatile compared to the exchange rate dynamics of Morocco and Tunisia This latter is the lowest in the exchange rate.\u003c/p\u003e \u003cp\u003eThe results of the fractional integration test applied to exchange rate data on the one hand and consumer prices on the other hand, in Algeria, Tunisia and Morocco showed that it is a fractional integration.\u003c/p\u003e \u003cp\u003eOur results also showed that Algeria, Tunisia and Morocco has a co-integration relationship between exchange rates and the inflation rate, where the persistence rate was estimated in Algeria of 1.171, it is greater than 1, which means that the variance is not fixed, as exchange rate shocks cause permanent inflation persistence in the Algeria. And the persistence rate was estimated in Tunisia of 0.635, and is bounded between 0.50 and 1, This means that the Cointegration process is highly stable but exhibits long memory with a high level of persistence in the cointegration. Hence, the variation in the inflation rate in Tunisia will persist for a longer period due to the shock of exchange rates before eventually is fading away. And the persistence rate was estimated in Morocco of 0.308, and is bounded between 0 and 0.50, this means The cointegration process is also stationary but it shows a long memory with low persistence in the cointegration This indicates that the effect of the exchange rate shock on the inflation rate persistence in a Morocco will last for a short period. In other words, the change in the persistence of inflation rate in Morocco due to an exchange rate shock will fade away within a short period.\u003c/p\u003e \u003cp\u003eThese results come to confirm the hypotheses that we presented and are also compatible with various economic theories.\u003c/p\u003e \u003cp\u003eThe results of this study will open horizons for researchers to study the impact of other (external) structural factors in the inflation rate persistence in North African countries in order to understand more broadly the dynamics of the inflation rate persistence in in this Region.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eAdenstedt, R. K. (1974). On large-sample estimation for the mean of a stationary random sequence. \u003cem\u003eThe Annals of Statistics\u003c/em\u003e, 1095\u0026ndash;1107.\u003c/li\u003e\n\u003cli\u003eAlogoskoufis, G. S., \u0026amp; Smith, R. (1991). 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Inflation persistence, inflation expectations, and monetary policy in China. \u003cem\u003eEconomic Modelling\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 622\u0026ndash;629. https://doi.org/10.1016/j.econmod.2010.06.009\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University Centre of Maghnia","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 rate persistence, exchange rate shocks, fractional cointegration, North African countries","lastPublishedDoi":"10.21203/rs.3.rs-4308266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4308266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to analyse the relationship between exchange rate and inflation rates persistence in North African countries (Algeria, Tunisia, and Morocco) to determine the extent of inflation persistence in face to exchange rate shocks from January 1987 to April 2023 using the recently developed Fractional Cointegration Model. This topic was chosen due to its significance for monetary policymakers, investors, financial analysts and academics in understanding the dynamics of inflation persistence in North African countries. Our results also showed that Algeria, Tunisia and Morocco has a co-integration relationship between exchange rate and the inflation rate, As exchange rate shocks cause permanent inflation persistence in Algeria and the variation in the inflation rate in Tunisia will persistence for a longer period due to the shock of exchange rate before eventually fading away and the change in the persistence of inflation rate in a Morocco due to an exchange rate shock will fade away within a short period.\u003c/p\u003e","manuscriptTitle":"Exchange prices shocks and inflation rate persistence for north African countries: a fractional cointegration relationship","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 07:19:53","doi":"10.21203/rs.3.rs-4308266/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":"9beded5d-71f4-4a87-bccb-41478bbdc207","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31020307,"name":"Econometrics"}],"tags":[],"updatedAt":"2024-04-24T07:19:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-24 07:19:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4308266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4308266","identity":"rs-4308266","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}