Inflation and pandemic in Spain

Inflation and pandemic in Spain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inflation and pandemic in Spain Leonardo Tariffi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6576910/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 paper shows what the main inflation macroeconomics drivers in Spain are. Even if there has been a less than two-digit inflation in the last three decades, it can be emphasized the fact that the inflation rate has raised and declined rapidly in recent years because of its fundamental determinants. Main reasons behind the behaviour of the consumption price index are related to higher prices in the energy sector and a higher government expenditure, particularly after the post-pandemic economy re-opening. Proxy variables such as oil prices free on board in the European Brent market, the 12 months Euribor interest rate of the Economic and Monetary Union, the nominal gross domestic product, the government expenditure of the public administration, and fiscal deficits in terms of the gross domestic product are those variables in which the consumer price index depends on. Changes on interest rates have managed to stabilized inflation rates once again, thereby diminishing the percentage change in the consumer price index. JEL Classification: E31, E58, L71 Macroeconomics Econometrics International Economics The inflation rate Consumer Price Index Determinants Central Banks Hydrocarbon Fuels Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction What are the main inflation drivers in macroeconomics? How can it be explained the recent increase in the level of prices? Are international energy market’s prices related to the Consumer Price Index (CPI) in Spain? Has the pandemic affected the level of the inflation rate? In macroeconomics, the inflation rate is one of the main variables of the economy. It can be measured as the percentage change on the CPI. It is directly related to the demand and the supply of goods and services purchased or otherwise acquired by households. Its evolution over time could affect both the purchasing power of an individual and the production cost in companies. The analysis of prices behaviour makes it possible to determine which are the fundamental variables that are closely linked to the inflation's evolution over time. In Spain, the annual variation in percentages of the CPI can be observed in Fig. 1. During the studied period, a greater variability of inflation is observed until the beginning of the 80s. After the first years of this decade, goods and services prices have been growing in a very stable rate and inflation remained relatively low for almost the last four decades. In fact, annual inflation has been lower than the two digits since May 1985. In June 2022, the inflation rate reached the 10% annual change once again and exceeded it for a short period of time. Notice that there was also negative general inflation at least during three intervals in the last two decades. The CPI percentage change reached below zero levels in 2009, from 2014 to 2016, and especially during the Covid pandemic in 2020. The CPI is an index used to measure the behaviour of prices over the time paid by consumers for a market basket of consumer’s goods and services. According to the Spanish National Institute of Statistics (INE), the CPI can be divided into 12 sectoral, different groups in Spain. The breakdown of the CPI into sub-groups over the last 20 years can be seen in Fig. 2 and the appendix . Although the annual percentage variation of the general price index remains relatively stable month to month during this period, some of the sub-groups published by the INE show greater volatility. In Fig. 2, it can be observed the CPI in annual percentage changes for the group 04 on housing, water, electricity, gas and other fuels as well as the overall inflation rate. This subgroup peaks the highest inflation rate in March 2022 when it reaches 33.1%. Notice that the data from group 04 shows not only higher variability than the general index but also a significant increase in prices during the pandemic. It could be seen as a leading indicator of the inflation rate in Spain during the observed period of time. The inflation time series for the remaining groups can be found in the appendix (figures from A1 to A11). In these figures, there were mainly two different groups where the sectoral inflation rate was obviously higher that the overall index during the Covid pandemic as in Fig. 2. They were the CPI for food and non-alcoholic beverages and the CPI for transport. In figures A1 and A6 in appendix , the CPI corresponding to the food and non-alcoholic beverages subgroup and the CPI for the transport subgroup both reach a maximum percentage change ​​of 16.6% in February 2023 and 19.2% in June 2022, respectively. These figures also show that there is a significant variability as a consequence of both increasing and decreasing prices in goods and services in economic activities related to both food and non-alcoholic beverages and transport sectors. This volatility in prices are similar to those observed in Fig. 2. There is plenty economic literature to explain the behaviour of aggregate prices. Keynes ( 1936 ) argued that the production level -which is determined by the marginal propensity to consume, the marginal efficiency of capital, and the interest rate- can be increased by increasing the government expenditure in times of economic depression, thereby the more public sector’s expenditure, the higher the aggregate demand, and the higher the level of aggregate production. However, Friedman ( 1963 ) explains that the increase in demand and aggregate consumption because of changes on monetary variables -such as money supply or the nominal interest rate- may result, not in a production's increase, but in an increase of the general price level of good and services. Galí ( 1999 ) uses a structural econometric analysis in a New Keynesian Phillips curve framework to link inflation not only to the output gap but also to inflation itself (expected and lagged) and the marginal cost. The CPI appears to be related to production costs as a consequence of decreasing and increasing energy prices on the supply side. Figure 3 shows oil prices free on board (FOB) in the Brent European market according to the U.S. Energy Information Administration (EIA). There are three periods of time where the price was higher than 100 dollars per barrel in Fig. 3, the first one goes from March 2008 to August 2008, the second one is related to period that starts in February 2011 and ends in August 2014 (excluding month June 2012), and the last one starts in March 2022 until August 2022. Notice that Russia invaded Ukraine in February 2022. Taking into account data from the European Money Market Institute (EMMI), the Central Bank of Spain (BdE) publishes the Euribor in a monthly basis (the interest rate at which a selection of European banks lend one another). Figure 4 shows such an interest rate for 12 months related to the money market in the Economic and Monetary Union. Besides the fact that there was a negative interest rate between February 2016 and March 2022, it is important to observe that there are three peaks in the data corresponding to May 2002, July 2008, and October 2023. Ascari et al ( 2023 ) distinguishes three chronological phases to explain inflation in recent years in the Euro Area. According to the authors, there were three phases during the Covid pandemic. The covid shock (both first and second quarters in 2020) as phase I, the reopening of the economy (from third quarter 2020 to third quarter 2021) as phase II, and the post reopening (from the fourth quarter 2021 onwards) as phase III. Specifically, the behaviour of output and inflation appears consistent with (i) aggregate demand experiencing a negative shock in phase I followed by a sequence of positive shocks in phase II and III, and (ii) aggregate supply experiencing a negative shock in phase I followed by a positive shock in phase II and another negative shock in phase III. According to the INE, the Gross Domestic Product (GDP) exhibited an unprecedented decline during the phase I in Spain. This reduction was equivalent to 22.8% from 326,698 millions of euros at current prices from the fourth quarter of 2019 to 252,148 millions of euros at current prices in the second quarter of 2020. Using an index built by INE to measure the industrial production in Spain, it has been transformed the quarterly GDP to a monthly basis in order to plot the data shows in Fig. 5. In this chart, it can be observed that the aforementioned GDP’s negative change during the phase I is different from the − 0.34% CPI annual decrease in June 2020. It suggests that there was not inflationary effects of a decreasing supply during this period of time. Moreover, the GDP grew at 8.21% y-to-y in the third quarter of 2021 when the CPI showed an annual change of 4.01% in September 2021. The economic activity has been growing in average 2.21% q-to-q since the beginning of phase III. The inflation rate reached a peak of 10.77% in July 2022 and decreased until it gets an average of around 3.2% during the first half of 2024. Therefore, there was a positive and increasing inflation during the reopening of the economy in phase II and a positive and even two-digit inflation after the post reopening of the economy in phase III. Figure 6 shows the government expenditure (black line) as well as the fiscal deficit (segmented line). These time series are published in the Public Administration Statistics of the BdE in thousands of euros based in data from the Spanish Ministry of Economics and Finance (IGAE) in a monthly basis from January 2004 onwards. Observations for the first two years of the sample has been calculated using the government cash flow month by month. The level of government spending has been growing consecutively since the beginning of the selected period of time and its volatility is always higher, particularly after June 2020. Notice that the fiscal surplus reached its maximum level of 15,626,000 thousands euros in December 2011 and the highest fiscal deficit corresponds to July 2009 when it was equal to -23,013,000 thousands euros. This research’s main goal is to present an econometric model with main determinants of the consumption price index behaviour to show that recent changes on inflation depends positively not only on energy prices but also on the government expenditure and negatively on the interest rate. The methodology is based on cointegration techniques in the long run when the OLS method with error correction mechanisms is applied to find the short term relationship. 2 The model The econometric model for inflation and its main determinants is based on two main approaches. The first approach applies a cointegration process (Johansen, 1988 and 1991 ) to find a long-term relationship between the macroeconomic variables (Engle and Granger, 1987 ) under the assumption of unrestricted intercept and restricted trend as in Pesaran, Shin and Smith ( 2000 ). The second approach uses the methodology of ordinary least squares (OLS) with error correction mechanism (ECM) to find the short-term relationship employing a "from general to specific" procedure as specified by Campos et al. ( 2005 ) and Hendry ( 2024 ). In addition, unit root tests are carried out based on the augmented Dickey-Fuller test (Dickey and Fuller, 1979 and 1981 ) considering the optimal number of lags calculated through the Schwarz information criterion and the critical values ​​established by MacKinnon at 1, 5 and 10% (MacKinnon, 2010 ). The proxy variables for aggregate prices and its determinants are the following (for both the long and the short term): CPI: The Consumer Price Index as general, aggregate, consumer prices. It is an index from the Spanish National Institute of Statistics (INE). Brent: The Europe Brent Spot Price FOB (dollars per barrel) as energy prices from the U.S. Energy Information Administration (EIA). Euribor: A 12 month interest rate in percentages as a monetary variable from the European Money Market Institute (EMMI). This data is published by the Central Bank of Spain (BdE) in a monthly basis. GDP: The gross domestic product (millions euros at current prices) as an economic activity variable from the INE. Using an index built by INE to measure the industrial production, this time series has been transformed from quarterly to monthly data. G: The government expenditure (thousands euros) from the Public Administration Statistics of the BdE based in data from the Spanish Ministry of Economics and Finance (IGAE). Deficit: The fiscal deficit from the Public Administration Statistics of the BdE based in data from the Spanish Ministry of Economics and Finance (IGAE) divided by the GDP. The first two years of the sample has been calculated using the government cash flow month by month. 3 Empirical evidence The software EViews 12 (Startz, 1994) is used to study the stationarity of the variables in the model. The standard augmented Dickey-Fuller test evaluates unit roots. The null hypothesis corresponds to one unit root (I(1)). The Schwarz information criterion is taken into account to choose the optimal number of lags with a maximum number of 15 lags. The intercept is included in the test equation. The critical values from MacKinnon (2010) are considered to estimate the probabilities associated to the t statistics. Table 1 shows the results. It can be observed that all variables are integrated of order 1 I(1) apart from the CPI. The CPI is integrated of order 2. Table 1 Unit Root Test (Augmented Dickey-Fuller) Variable Order of integration CPI I(2) Brent I(1) Euribor I(1) GDP I(1) G I(1) Deficit I(1) Source: Author's own calculations Converting CPI to I(1), the multivariate cointegrating autoregressive vectors (VAR) are found using the software Microfit 5.5 (Pesaran and Pesaran, 2009) under the assumption of unrestricted intercept and restricted trend as calculated in Pesaran, Shin and Smith (2000). In Tables 2 and 3, the results of the cointegration test run with variables lagged only once can be observed. Taking into account the fact that the r statistic is greater than the critical values calculated as an asymptotically non-standard distribution according to the Johansen methodology (Johansen, 1988 and 1991) in the case of at least 3 vectors (when r < = 3), the null hypothesis of 3 cointegrated long run relationships are not rejected (see Table 2). The long-term relationship between the CPI and its fundamental determinants can be seen in Table 3. This table shows 3 vectors with the corresponding cointegrating parameters values that have been found. Apart from the parameter in the variable G, all coefficients in vector 2 exhibit correct signs according to the theory. The positive relationship between CPI and G will be found in the short term model introducing variables with a higher number of lags. After calculating the ECM with vector 2, the coefficients of the variables for the short-term model are estimated with the OLS method using the robust econometric procedure “from the general to the specific” as in Campos et al. (2005) and Hendry (2024). The software EViews 12 (Startz, 1994) is used once again to run the model with all variables integrated in order 0. Table 4 shows the results. Considering the probabilities associated to the t statistic, it can be observed that all variables are statistically significant at 10% (apart from the intercept and the ECM). Table 4: Ordinary Least Squares (OLS) regression model Dependent Variable D2CPI Sample (adjusted): 2003M01-2024M09. Included observacions: 261 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -0.006400 0.030898 -0.207149 0.8361 D2CPI(-1) -0.407911 0.046145 -8.839701 0.0000 D2CPI(-3) -0.155297 0.050038 -3.103569 0.0021 D2CPI(-4) -0.291315 0.050976 -5.714739 0.0000 D2CPI(-6) 0.239530 0.047531 5.039423 0.0000 DBRENT 0.034198 0.003983 8.585165 0.0000 DBRENT(-4) 0.009052 0.004326 2.092464 0.0374 DEURIBOR(-2) -0.405072 0.232646 -1.741149 0.0829 DEURIBOR(-3) -0.703238 0.285331 -2.464641 0.0144 DEURIBOR(-4) 1.026161 0.232612 4.411477 0.0000 DGDP(-1) 1.49E-05 2.82E-06 5.279625 0.0000 DGDP(-5) 1.46E-05 2.86E-06 5.094005 0.0000 DGDP(-6) 5.23E-06 2.71E-06 1.932731 0.0544 DGDP(-11) 1.40E-05 3.24E-06 4.331321 0.0000 DG 1.34E-08 5.52E-09 2.421061 0.0162 DG(-3) 1.16E-08 5.30E-09 2.184351 0.0299 DG(-8) -1.18E-08 5.81E-09 -2.027517 0.0437 DDEFICIT 0.015927 0.003826 4.162518 0.0000 DDEFICIT(-7) 0.005457 0.003153 1.730434 0.0848 ECM -0.597808 0.603902 -0.989909 0.3232 R-squared 0.726645 Mean dependent var -0.003594 Adjusted R-squared 0.705094 S.D. dependent var 0.738742 S.E. of regression 0.401175 Akaike info criterion 1.084697 Sum squared resid 38.78693 Schwarz criterion 1.357840 Log likelihood -121.5529 Hannan-Quinn criter. 1.194491 F-statistic 33.71777 Durbin-Watson stat 2.290416 Prob (F-statistic) 0.000000 Source: Author's own calculations. The CPI has been introduced lagged in the econometric model to proof both the inertia and persistence on inflation. Notice that D stands for first differences, D2 are second differences, values in parentheses indicate level of lags, and the coefficient of the ECM has a negative sign. All variables exhibit correct signs according to the theory. Increasing inflation are related to increasing energy prices, a decreasing interest rate, a higher government expenditure, and a higher deficit. The relationship between the CPI and the GDP appears to be positive short term. The model fits the data by more than 70% (R 2 = 0.73). 4 Conclusions The main interest in this research has been to explain with certainty the behaviour of the inflation in Spain not only in the short term but also in the long run. The energy sector, an efficient monetary policy, level of aggregate production, expansive fiscal policy and several years of deficit are some of the fundamental determinants of inflation, particularly during the pandemic. In fact, the main reasons behind the consumption price index increase after the post-pandemic economy re-opening are related to higher prices in the energy sector. Proxies like oil prices in the European Brent market, the Euribor interest rate, the domestic gross domestic product, the domestic government expenditure, and the fiscal deficit from the national public administration are those variables in which the consumer price index depends on. Further research could be done in order to find if the interaction between the demand and the supply of good and services has structural changes’ effect on inflation. Declarations The author declare that no funds, grants, or other support were received during the preparation of this manuscript. The author have no relevant financial or non-financial interests to disclose. References Ascari, G., & Sbordone, A. M. (2014). The Macroeconomics of Trend Inflation. Journal of Economic Literature, 52 (3), 679–739. Ascari, G., Bonomolo, P., Hoeberichts, M., & Trezzi, R. (2023). The Euro Area Great Inflation Surge. De Nederlandsche Bank Analysis Series , 1-21. Campos, J., Ericsson, N., y Hendry, D. (2005). General-to-specific modelling: an overview and selected bibliography, International Finance Discussion Papers , 838, Board of Governors of the Federal Reserve System (U.S.). Dickey, D., & Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74 (366), 427-431. Dickey, D., & Fuller, W. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49 (4), 1057-1072. Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55 (2), 251-276. Friedman, M. (1963). Inflation: Causes and Consequences. Asia Publishing House for the Council for Economic Education in Collected Works of Milton Friedman Project, n. 2016c21.1266. Galí, J., & Gertler, M. (1999). Inflation dynamics: A structural econometric analysis. Journal of Monetary Economics 44 , 195-222. Hendry D., (2024). A Brief History of General-to-specific Modelling, Oxford Bulletin of economics and statistics, 86 (1), 0305-9049. doi: 10.1111/obes.12578 Johansen, S. (1988). Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12 , 231-254. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models, Econometrica, 59 (6), 1551-1580. Keynes, J. M. (1936). The General Theory of Employment, Interest, and Money. International Relations and Security Network in ISN ETH Zurich, pp. 190. Lagarde, C., & De Guindos, L. (2022). Monetary policy statement in “Combined monetary policy decisions and statement”. European Central Bank, Press conference , 8 September 2022. MacKinnon, J. (2010). Critical Values for Cointegration Tests, Queen’s Economics Department Working Paper, No. 1227. Pesaran, B., & Pesaran, M. H. (2009). Time Series Econometrics using Microfit 5.0: A User's Manual, Oxford University Press . Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22 , 265-312. Pesaran, M. H., Shin, Y., & Smith, R. (2000). Structural analysis of vector error correction models with exogenous I(1) variables, Journal of Econometrics, 97 , 293-343. Startz, R. (1994). EViews Illustrated, IHS Global Inc , pp. 430. Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6576910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451006310,"identity":"065570a2-9d5d-483d-a916-ffd7415ce31b","order_by":0,"name":"Leonardo Tariffi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACPhCRYCPHw8/AA2SxEaGFDaQoIc2YR7KBJC0MacYMBgeI1iLf/OzDgwQDGeMbuQcYPpQdJsYWNuMZCQkGPGY38hIYZ5wjSguDMUPijz9ALTkGzLxtRGlh/8wAssV4BlDLX+K08BiDtRhIALUwEqclpxisReLMu4SDPefSCWvhZz6+mfFHgoE9f3vuwQc/yqwJa0EBB0hUPwpGwSgYBaMAFwAAJzowbPqZVc4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1241-6527","institution":"University of Barcelona","correspondingAuthor":true,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"Tariffi","suffix":""}],"badges":[],"createdAt":"2025-05-02 09:05:50","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-6576910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6576910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81935844,"identity":"85d17a85-5807-4c8e-95f2-0edc8428949c","added_by":"auto","created_at":"2025-05-05 06:01:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89639,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/60c62f136a950ce6831df2d6.png"},{"id":81935656,"identity":"449177f3-ebf6-4355-a145-e5692ad33e96","added_by":"auto","created_at":"2025-05-05 06:01:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96656,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/c07c4feabc4b64495a2d48df.png"},{"id":81935862,"identity":"dbf7fa31-3a4f-4fde-88e8-22ac83260fdc","added_by":"auto","created_at":"2025-05-05 06:01:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58466,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/29c97422d80894a18d868a6c.png"},{"id":81935840,"identity":"562960a4-8d8f-48db-a361-cd992ad02333","added_by":"auto","created_at":"2025-05-05 06:01:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70928,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/891e4846d5bb6e29d2a29008.png"},{"id":81935774,"identity":"7de2b02e-5353-4015-b522-735471ffb3db","added_by":"auto","created_at":"2025-05-05 06:01:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116144,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/b6ea3243e7703d8a2517de30.png"},{"id":81935760,"identity":"f19ed401-9e93-4a1a-8622-6374297db6a8","added_by":"auto","created_at":"2025-05-05 06:01:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":173083,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/07b4e5107fdfe9d8da4dfbd2.png"},{"id":81936612,"identity":"add2044d-94f8-4dde-af07-ebcef0eb1f71","added_by":"auto","created_at":"2025-05-05 06:08:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1314367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/0c5b2c59-5848-4930-8c04-f1d02839af62.pdf"},{"id":81935542,"identity":"4fadd5a0-4a31-4a21-914a-7d4e67b10c83","added_by":"auto","created_at":"2025-05-05 06:01:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":282807,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6576910/v1/3348bb7199d30015771c907f.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInflation and pandemic in Spain\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWhat are the main inflation drivers in macroeconomics? How can it be explained the recent increase in the level of prices? Are international energy market\u0026rsquo;s prices related to the Consumer Price Index (CPI) in Spain? Has the pandemic affected the level of the inflation rate?\u003c/p\u003e \u003cp\u003eIn macroeconomics, the inflation rate is one of the main variables of the economy. It can be measured as the percentage change on the CPI. It is directly related to the demand and the supply of goods and services purchased or otherwise acquired by households. Its evolution over time could affect both the purchasing power of an individual and the production cost in companies. The analysis of prices behaviour makes it possible to determine which are the fundamental variables that are closely linked to the inflation's evolution over time.\u003c/p\u003e \u003cp\u003eIn Spain, the annual variation in percentages of the CPI can be observed in Fig.\u0026nbsp;1. During the studied period, a greater variability of inflation is observed until the beginning of the 80s. After the first years of this decade, goods and services prices have been growing in a very stable rate and inflation remained relatively low for almost the last four decades. In fact, annual inflation has been lower than the two digits since May 1985. In June 2022, the inflation rate reached the 10% annual change once again and exceeded it for a short period of time. Notice that there was also negative general inflation at least during three intervals in the last two decades. The CPI percentage change reached below zero levels in 2009, from 2014 to 2016, and especially during the Covid pandemic in 2020.\u003c/p\u003e \u003cp\u003eThe CPI is an index used to measure the behaviour of prices over the time paid by consumers for a market basket of consumer\u0026rsquo;s goods and services. According to the Spanish National Institute of Statistics (INE), the CPI can be divided into 12 sectoral, different groups in Spain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe breakdown of the CPI into sub-groups over the last 20 years can be seen in Fig.\u0026nbsp;2 and the \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e. Although the annual percentage variation of the general price index remains relatively stable month to month during this period, some of the sub-groups published by the INE show greater volatility.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;2, it can be observed the CPI in annual percentage changes for the group 04 on housing, water, electricity, gas and other fuels as well as the overall inflation rate. This subgroup peaks the highest inflation rate in March 2022 when it reaches 33.1%. Notice that the data from group 04 shows not only higher variability than the general index but also a significant increase in prices during the pandemic. It could be seen as a leading indicator of the inflation rate in Spain during the observed period of time.\u003c/p\u003e \u003cp\u003eThe inflation time series for the remaining groups can be found in the \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e (figures from A1 to A11). In these figures, there were mainly two different groups where the sectoral inflation rate was obviously higher that the overall index during the Covid pandemic as in Fig.\u0026nbsp;2. They were the CPI for food and non-alcoholic beverages and the CPI for transport.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn figures A1 and A6 in \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e, the CPI corresponding to the food and non-alcoholic beverages subgroup and the CPI for the transport subgroup both reach a maximum percentage change ​​of 16.6% in February 2023 and 19.2% in June 2022, respectively. These figures also show that there is a significant variability as a consequence of both increasing and decreasing prices in goods and services in economic activities related to both food and non-alcoholic beverages and transport sectors. This volatility in prices are similar to those observed in Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eThere is plenty economic literature to explain the behaviour of aggregate prices. Keynes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1936\u003c/span\u003e) argued that the production level -which is determined by the marginal propensity to consume, the marginal efficiency of capital, and the interest rate- can be increased by increasing the government expenditure in times of economic depression, thereby the more public sector\u0026rsquo;s expenditure, the higher the aggregate demand, and the higher the level of aggregate production. However, Friedman (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) explains that the increase in demand and aggregate consumption because of changes on monetary variables -such as money supply or the nominal interest rate- may result, not in a production's increase, but in an increase of the general price level of good and services. Gal\u0026iacute; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) uses a structural econometric analysis in a New Keynesian Phillips curve framework to link inflation not only to the output gap but also to inflation itself (expected and lagged) and the marginal cost.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CPI appears to be related to production costs as a consequence of decreasing and increasing energy prices on the supply side. Figure\u0026nbsp;3 shows oil prices free on board (FOB) in the Brent European market according to the U.S. Energy Information Administration (EIA).\u003c/p\u003e \u003cp\u003eThere are three periods of time where the price was higher than 100 dollars per barrel in Fig.\u0026nbsp;3, the first one goes from March 2008 to August 2008, the second one is related to period that starts in February 2011 and ends in August 2014 (excluding month June 2012), and the last one starts in March 2022 until August 2022. Notice that Russia invaded Ukraine in February 2022.\u003c/p\u003e \u003cp\u003eTaking into account data from the European Money Market Institute (EMMI), the Central Bank of Spain (BdE) publishes the Euribor in a monthly basis (the interest rate at which a selection of European banks lend one another). Figure\u0026nbsp;4 shows such an interest rate for 12 months related to the money market in the Economic and Monetary Union. Besides the fact that there was a negative interest rate between February 2016 and March 2022, it is important to observe that there are three peaks in the data corresponding to May 2002, July 2008, and October 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAscari et al (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) distinguishes three chronological phases to explain inflation in recent years in the Euro Area. According to the authors, there were three phases during the Covid pandemic. The covid shock (both first and second quarters in 2020) as phase I, the reopening of the economy (from third quarter 2020 to third quarter 2021) as phase II, and the post reopening (from the fourth quarter 2021 onwards) as phase III. Specifically, the behaviour of output and inflation appears consistent with (i) aggregate demand experiencing a negative shock in phase I followed by a sequence of positive shocks in phase II and III, and (ii) aggregate supply experiencing a negative shock in phase I followed by a positive shock in phase II and another negative shock in phase III.\u003c/p\u003e \u003cp\u003eAccording to the INE, the Gross Domestic Product (GDP) exhibited an unprecedented decline during the phase I in Spain. This reduction was equivalent to 22.8% from 326,698 millions of euros at current prices from the fourth quarter of 2019 to 252,148 millions of euros at current prices in the second quarter of 2020. Using an index built by INE to measure the industrial production in Spain, it has been transformed the quarterly GDP to a monthly basis in order to plot the data shows in Fig.\u0026nbsp;5. In this chart, it can be observed that the aforementioned GDP\u0026rsquo;s negative change during the phase I is different from the \u0026minus;\u0026thinsp;0.34% CPI annual decrease in June 2020. It suggests that there was not inflationary effects of a decreasing supply during this period of time.\u003c/p\u003e \u003cp\u003eMoreover, the GDP grew at 8.21% y-to-y in the third quarter of 2021 when the CPI showed an annual change of 4.01% in September 2021. The economic activity has been growing in average 2.21% q-to-q since the beginning of phase III. The inflation rate reached a peak of 10.77% in July 2022 and decreased until it gets an average of around 3.2% during the first half of 2024. Therefore, there was a positive and increasing inflation during the reopening of the economy in phase II and a positive and even two-digit inflation after the post reopening of the economy in phase III.\u003c/p\u003e \u003cp\u003eFigure 6 shows the government expenditure (black line) as well as the fiscal deficit (segmented line). These time series are published in the Public Administration Statistics of the BdE in thousands of euros based in data from the Spanish Ministry of Economics and Finance (IGAE) in a monthly basis from January 2004 onwards. Observations for the first two years of the sample has been calculated using the government cash flow month by month. The level of government spending has been growing consecutively since the beginning of the selected period of time and its volatility is always higher, particularly after June 2020. Notice that the fiscal surplus reached its maximum level of 15,626,000 thousands euros in December 2011 and the highest fiscal deficit corresponds to July 2009 when it was equal to -23,013,000 thousands euros.\u003c/p\u003e\u003cp\u003eThis research\u0026rsquo;s main goal is to present an econometric model with main determinants of the consumption price index behaviour to show that recent changes on inflation depends positively not only on energy prices but also on the government expenditure and negatively on the interest rate. The methodology is based on cointegration techniques in the long run when the OLS method with error correction mechanisms is applied to find the short term relationship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2 The model","content":"\u003cp\u003eThe econometric model for inflation and its main determinants is based on two main approaches. The first approach applies a cointegration process (Johansen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1988\u003c/span\u003e and \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) to find a long-term relationship between the macroeconomic variables (Engle and Granger, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) under the assumption of unrestricted intercept and restricted trend as in Pesaran, Shin and Smith (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The second approach uses the methodology of ordinary least squares (OLS) with error correction mechanism (ECM) to find the short-term relationship employing a \"from general to specific\" procedure as specified by Campos et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Hendry (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, unit root tests are carried out based on the augmented Dickey-Fuller test (Dickey and Fuller, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1979\u003c/span\u003e and \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) considering the optimal number of lags calculated through the Schwarz information criterion and the critical values ​​established by MacKinnon at 1, 5 and 10% (MacKinnon, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe proxy variables for aggregate prices and its determinants are the following (for both the long and the short term):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCPI: The Consumer Price Index as general, aggregate, consumer prices. It is an index from the Spanish National Institute of Statistics (INE).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBrent: The Europe Brent Spot Price FOB (dollars per barrel) as energy prices from the U.S. Energy Information Administration (EIA).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEuribor: A 12 month interest rate in percentages as a monetary variable from the European Money Market Institute (EMMI). This data is published by the Central Bank of Spain (BdE) in a monthly basis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGDP: The gross domestic product (millions euros at current prices) as an economic activity variable from the INE. Using an index built by INE to measure the industrial production, this time series has been transformed from quarterly to monthly data.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eG: The government expenditure (thousands euros) from the Public Administration Statistics of the BdE based in data from the Spanish Ministry of Economics and Finance (IGAE).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDeficit: The fiscal deficit from the Public Administration Statistics of the BdE based in data from the Spanish Ministry of Economics and Finance (IGAE) divided by the GDP. The first two years of the sample has been calculated using the government cash flow month by month.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"3 Empirical evidence","content":"\u003cp\u003eThe software EViews 12 (Startz, 1994) is used to study the stationarity of the variables in the model. The standard augmented Dickey-Fuller test evaluates unit roots. The null hypothesis corresponds to one unit root (I(1)). The Schwarz information criterion is taken into account to choose the optimal number of lags with a maximum number of 15 lags. The intercept is included in the test equation. The critical values from MacKinnon (2010) are considered to estimate the probabilities associated to the \u003cem\u003et\u003c/em\u003e statistics. Table 1 shows the results. It can be observed that all variables are integrated of order 1 I(1) apart from the CPI. The CPI is integrated of order 2.\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eUnit Root Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(Augmented Dickey-Fuller)\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\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrder of integration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuribor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeficit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSource: Author\u0026apos;s own calculations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eConverting CPI to I(1), the multivariate cointegrating autoregressive vectors (VAR) are found using the software Microfit 5.5 (Pesaran and Pesaran, 2009) under the assumption of unrestricted intercept and restricted trend as calculated in Pesaran, Shin and Smith (2000). In Tables 2 and 3, the results of the cointegration test run with variables lagged only once can be observed. Taking into account the fact that the \u003cem\u003er\u003c/em\u003e statistic is greater than the critical values calculated as an asymptotically non-standard distribution according to the Johansen methodology (Johansen, 1988 and 1991) in the case of at least 3 vectors (when r\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;3), the null hypothesis of 3 cointegrated long run relationships are not rejected (see Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003eThe long-term relationship between the CPI and its fundamental determinants can be seen in Table 3. This table shows 3 vectors with the corresponding cointegrating parameters values that have been found. Apart from the parameter in the variable G, all coefficients in vector 2 exhibit correct signs according to the theory. The positive relationship between CPI and G will be found in the short term model introducing variables with a higher number of lags.\u003c/div\u003e\n\u003cp\u003eAfter calculating the ECM with vector 2, the coefficients of the variables for the short-term model are estimated with the OLS method using the robust econometric procedure \u0026ldquo;from the general to the specific\u0026rdquo; as in Campos et al. (2005) and Hendry (2024). The software EViews 12 (Startz, 1994) is used once again to run the model with all variables integrated in order 0. Table 4 shows the results. Considering the probabilities associated to the \u003cem\u003et\u003c/em\u003e statistic, it can be observed that all variables are statistically significant at 10% (apart from the intercept and the ECM).\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eTable 4: Ordinary Least Squares (OLS) regression model Dependent Variable D2CPI Sample (adjusted): \u0026nbsp;2003M01-2024M09. \u0026nbsp;Included observacions: 261 after adjustments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.207149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD2CPI(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.407911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.839701\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 align=\"left\"\u003e\n \u003cp\u003eD2CPI(-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.155297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.103569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD2CPI(-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.291315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.714739\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 align=\"left\"\u003e\n \u003cp\u003eD2CPI(-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.239530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.039423\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 align=\"left\"\u003e\n \u003cp\u003eDBRENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.585165\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 align=\"left\"\u003e\n \u003cp\u003eDBRENT(-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.092464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEURIBOR(-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.405072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.232646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.741149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEURIBOR(-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.703238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.285331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.464641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEURIBOR(-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.026161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.232612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.411477\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 align=\"left\"\u003e\n \u003cp\u003eDGDP(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.279625\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 align=\"left\"\u003e\n \u003cp\u003eDGDP(-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.094005\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 align=\"left\"\u003e\n \u003cp\u003eDGDP(-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.23E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.71E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.932731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDGDP(-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.24E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.331321\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 align=\"left\"\u003e\n \u003cp\u003eDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.52E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.421061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDG(-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.30E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.184351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDG(-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.18E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.81E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.027517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDDEFICIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.162518\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 align=\"left\"\u003e\n \u003cp\u003eDDEFICIT(-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.730434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.597808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.989909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.726645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.705094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eS.D. dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.738742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS.E. of regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.401175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAkaike info criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.084697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum squared resid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.78693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSchwarz criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.357840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-121.5529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHannan-Quinn criter.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.194491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.71777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDurbin-Watson stat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.290416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb (F-statistic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSource: Author\u0026apos;s own calculations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe CPI has been introduced lagged in the econometric model to proof both the inertia and persistence on inflation. Notice that D stands for first differences, D2 are second differences, values in parentheses indicate level of lags, and the coefficient of the ECM has a negative sign. All variables exhibit correct signs according to the theory. Increasing inflation are related to increasing energy prices, a decreasing interest rate, a higher government expenditure, and a higher deficit. The relationship between the CPI and the GDP appears to be positive short term. The model fits the data by more than 70% (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.73).\u003c/p\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe main interest in this research has been to explain with certainty the behaviour of the inflation in Spain not only in the short term but also in the long run. The energy sector, an efficient monetary policy, level of aggregate production, expansive fiscal policy and several years of deficit are some of the fundamental determinants of inflation, particularly during the pandemic. In fact, the main reasons behind the consumption price index increase after the post-pandemic economy re-opening are related to higher prices in the energy sector.\u003c/p\u003e \u003cp\u003eProxies like oil prices in the European Brent market, the Euribor interest rate, the domestic gross domestic product, the domestic government expenditure, and the fiscal deficit from the national public administration are those variables in which the consumer price index depends on.\u003c/p\u003e \u003cp\u003eFurther research could be done in order to find if the interaction between the demand and the supply of good and services has structural changes\u0026rsquo; effect on inflation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe author declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003eThe author have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAscari, G., \u0026amp; Sbordone, A. M. (2014). The Macroeconomics of Trend Inflation. \u003cem\u003eJournal of Economic Literature, 52\u003c/em\u003e(3), 679\u0026ndash;739.\u003c/li\u003e\n\u003cli\u003eAscari, G., Bonomolo, P., Hoeberichts, M., \u0026amp; Trezzi, R. (2023). The Euro Area Great Inflation Surge. \u003cem\u003eDe Nederlandsche Bank Analysis Series\u003c/em\u003e, 1-21.\u003c/li\u003e\n\u003cli\u003eCampos, J., Ericsson, N., y Hendry, D. (2005). General-to-specific modelling: an overview and selected bibliography, \u003cem\u003eInternational Finance Discussion Papers\u003c/em\u003e, 838, Board of Governors of the Federal Reserve System (U.S.).\u003c/li\u003e\n\u003cli\u003eDickey, D., \u0026amp; Fuller, W. (1979). Distribution of the estimators for autoregressive time series with a unit root, \u003cem\u003eJournal of the American Statistical Association, 74\u003c/em\u003e(366), 427-431.\u003c/li\u003e\n\u003cli\u003eDickey, D., \u0026amp; Fuller, W. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. \u003cem\u003eEconometrica, 49\u003c/em\u003e(4), 1057-1072.\u003c/li\u003e\n\u003cli\u003eEngle, R. F., \u0026amp; Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. \u003cem\u003eEconometrica, 55\u003c/em\u003e(2), 251-276.\u003c/li\u003e\n\u003cli\u003eFriedman, M. (1963). Inflation: Causes and Consequences. \u003cem\u003eAsia Publishing House for the Council for Economic Education\u003c/em\u003e in Collected Works of Milton Friedman Project, n. 2016c21.1266.\u003c/li\u003e\n\u003cli\u003eGal\u0026iacute;, J., \u0026amp; Gertler, M. (1999). Inflation dynamics: A structural econometric analysis. \u003cem\u003eJournal of Monetary Economics 44\u003c/em\u003e, 195-222.\u003c/li\u003e\n\u003cli\u003eHendry D., (2024). A Brief History of General-to-specific Modelling,\u003cem\u003e Oxford Bulletin of economics and statistics, 86\u003c/em\u003e(1), 0305-9049. doi: 10.1111/obes.12578\u003c/li\u003e\n\u003cli\u003eJohansen, S. (1988). Statistical analysis of cointegration vectors, \u003cem\u003eJournal of Economic Dynamics and Control, 12\u003c/em\u003e, 231-254.\u003c/li\u003e\n\u003cli\u003eJohansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models, \u003cem\u003eEconometrica, 59\u003c/em\u003e(6), 1551-1580.\u003c/li\u003e\n\u003cli\u003eKeynes, J. M. (1936). The General Theory of Employment, Interest, and Money. \u003cem\u003eInternational Relations and Security Network\u003c/em\u003e in ISN ETH Zurich, pp. 190.\u003c/li\u003e\n\u003cli\u003eLagarde, C., \u0026amp; De Guindos, L. (2022). Monetary policy statement in \u0026ldquo;Combined monetary policy decisions and statement\u0026rdquo;. \u003cem\u003eEuropean Central Bank, Press conference\u003c/em\u003e, 8 September 2022.\u003c/li\u003e\n\u003cli\u003eMacKinnon, J. (2010). Critical Values for Cointegration Tests, \u003cem\u003eQueen\u0026rsquo;s Economics Department Working Paper,\u003c/em\u003e No. 1227.\u003c/li\u003e\n\u003cli\u003ePesaran, B., \u0026amp; Pesaran, M. H. (2009). Time Series Econometrics using Microfit 5.0: A User\u0026apos;s Manual, \u003cem\u003eOxford University Press\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003ePesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. \u003cem\u003eJournal of Applied Econometrics, 22\u003c/em\u003e, 265-312.\u003c/li\u003e\n\u003cli\u003ePesaran, M. H., Shin, Y., \u0026amp; Smith, R. (2000). Structural analysis of vector error correction models with exogenous I(1) variables, \u003cem\u003eJournal of Econometrics, 97\u003c/em\u003e, 293-343.\u003c/li\u003e\n\u003cli\u003eStartz, R. (1994). EViews Illustrated, \u003cem\u003eIHS Global Inc\u003c/em\u003e, pp. 430.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"The inflation rate, Consumer Price Index, Determinants, Central Banks, Hydrocarbon Fuels","lastPublishedDoi":"10.21203/rs.3.rs-6576910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6576910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper shows what the main inflation macroeconomics drivers in Spain are. Even if there has been a less than two-digit inflation in the last three decades, it can be emphasized the fact that the inflation rate has raised and declined rapidly in recent years because of its fundamental determinants. Main reasons behind the behaviour of the consumption price index are related to higher prices in the energy sector and a higher government expenditure, particularly after the post-pandemic economy re-opening. Proxy variables such as oil prices free on board in the European Brent market, the 12 months Euribor interest rate of the Economic and Monetary Union, the nominal gross domestic product, the government expenditure of the public administration, and fiscal deficits in terms of the gross domestic product are those variables in which the consumer price index depends on. Changes on interest rates have managed to stabilized inflation rates once again, thereby diminishing the percentage change in the consumer price index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification: \u003c/strong\u003eE31, E58, L71\u003c/p\u003e","manuscriptTitle":"Inflation and pandemic in Spain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 06:00:16","doi":"10.21203/rs.3.rs-6576910/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":"ddf65315-cd3d-42a2-b2f4-0e024e8cf4f2","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47999803,"name":"Macroeconomics"},{"id":47999804,"name":"Econometrics"},{"id":47999805,"name":"International Economics"}],"tags":[],"updatedAt":"2025-05-05T06:00:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 06:00:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6576910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6576910","identity":"rs-6576910","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}