Az élelmiszerár-infláció meghatározó tényezői az Európai Unió 27 tagállamában
DOI:
https://doi.org/10.32976/stratfuz.2025.6Kulcsszavak:
élelmiszerárak, olajárak, élelemiszerinfláció, Európai Unió, panelbecslésAbsztrakt
Jelen kutatás célja az élelmiszerár-infláció főbb meghatározó tényezőinek a vizsgálata az Európai Unió 27 tagállamában 2001. január és 2022. május között. A kutatás a tőzsdei nyersolajárak és a valutaárfolyam-ingadozások szerepére helyezi a hangsúlyt. Az elemzéshez kiegyensúlyozott panel adatbázist készítettünk havi adatok felhasználásával. A korábbi vizsgálatok eredményei nyomán az alábbi becslési módszereket alkalmaztuk: véletlen hatások (RE), panelkorrigált standard hibák (PCSE), módosított legkisebb négyzetek kointegrált becslése (FMOLS), valamint dinamikus legkisebb négyzetek (DOLS) regressziós módszere. Eredményeink azt mutatták, hogy az élelmiszerár-inflációt az olajárak növekedése és a valutaárfolyam-ingadozás egyaránt jelentős mértékben befolyásolták.
Hivatkozások
AGOVINO, M., CASACCIA, M., CIOMMI, M., FERRARA, M., & MARCHESANO, K. (2019). Agriculture, climate change and sustainability: The case of EU-28. Ecological Indicators, 105, 525–543. https://doi.org/10.1016/j.ecolind.2018.04.064
ALEXANDER, P., ARNETH, A., HENRY, R., MAIRE, J., RABIN, S., & ROUNSEVELL, M. (2022). Increasing food prices may cause up to a million deaths in 2023, even if Ukraine food exports are restored. Research Square, (preprint). https://doi.org/10.21203/rs.3.rs-1851998/v1
BARROS, G. S. DE C., CARRARA, A. F., CASTRO, N. R., & SILVA, A. F. (2022). Agriculture and inflation: Expected and unexpected shocks. The Quarterly Review of Economics and Finance, 83, 178–188. https://doi.org/10.1016/j.qref.2021.12.002
BARUNÍK, J., & KLEY, T. (2019). Quantile coherency: A general measure for dependence between cyclical economic variables. The Econometrics Journal, 22(2), 131–152. https://doi.org/10.1093/ectj/utz002
BORKOWSKI, B., DUDEK, H., & SZCZESNY, W. (2008). Spatial Differentiation of Food Production Structure and Consumption Profile in Enlarged European Union. The Journal of Economic Asymmetries, 5(2), 145–156. https://doi.org/10.1016/j.jeca.2008.02.009
BREUSCH, T. S., & PAGAN, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies, 47, 239–253. https://doi.org/10.2307/2297111
DRUKKER, D. M. (2003). Testing for serial correlation in linear panel-data models. Stata Journal, 3(2), 168–177. https://doi.org/10.1177/1536867X0300300206
ECB. (2022). Supply chain disruptions and the effects on the global economy. ECB Economic Bulletin, 8/2021. https://www.ecb.europa.eu/pub/economic-bulletin/focus/2022/html/ecb.ebbox202108_01~e8ceebe51f.en.html (Letöltés ideje: 2023. február 9.)
EUROPEAN COUNCIL (2023). Infographic - Where does the EU’s gas come from? https://www.consilium.europa.eu/en/infographics/eu-gas-supply/ (Letöltés ideje: 2023. február 2.)
EUROSTAT (2022). Energy statistics - an overview. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_statistics_-_an_overview (Letöltés ideje: 2024. szeptember 1.)
EUROSTAT (2023). The EU imported 58% of its energy in 2020. https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20220328-2 (Letöltés ideje: 2023. január 8.)
FAOSTAT (2022). Consumer Price Indices. Monthly Food CPI. https://www.fao.org/faostat/en/#data/CP (Letöltés ideje: 2022. december 8.)
FRED (2023). Federal Reserve Bank of St. Louis. Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma (DCOILWTICO) https://fred.stlouisfed.org/series/DCOILWTICO (Letöltés ideje: 2023. január 14.)
GILLER, K., DELAUNE, T., SILVA, J., DESCHEEMAEKER, K., VEN, G. W. J., SCHUT, A. G. T., VAN WIJK, M., HAMMOND, J., HOCHMAN, Z., TAULYA, G., CHIKOWO, R., NARAYANAN, S., KISHORE, A., BRESCIANI, F., TEIXEIRA, H., ANDERSSON, J., & ITTERSUM, M. (2021). The future of farming: Who will produce our food? Food Security, 13., 1073-1099. https://doi.org/10.1007/s12571-021-01184-6
GIORDANI, P. E., ROCHA, N., & RUTA, M. (2016). Food prices and the multiplier effect of trade policy. Journal of International Economics, 101, 102–122. https://doi.org/10.1016/j.jinteco.2016.04.001
HANIF, W., AREOLA HERNANDEZ, J., SHAHZAD, S. J. H., & YOON, S.-M. (2021). Tail dependence risk and spillovers between oil and food prices. The Quarterly Review of Economics and Finance, 80, 195–209. https://doi.org/10.1016/j.qref.2021.01.019
HAUSMAN, J. A. (1978). Specification tests in econometrics. Econometrica, 46, 1251–1271. https://doi.org/10.2307/1913827
IBRAHIM, M. H. (2015). Oil and food prices in Malaysia: A nonlinear ARDL analysis. Agricultural and Food Economics, 3(1), 2. https://doi.org/10.1186/s40100-014-0020-3
JAWORSKI, K. (2021): Measuring food inflation during the COVID-19 pandemic in real time using online data: A case study of Poland. British Food Journal, 123(13). 260–280. o. https://doi.org/10.1108/BFJ-06-2020-0532
KAO, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90. 1-44. https://doi.org/10.1016/S0304-4076(98)00023-2
KARAKOTSIOS, A., KATRAKILIDIS, C., & KROUPIS, N. (2021). The dynamic linkages between food prices and oil prices. Does asymmetry matter? The Journal of Economic Asymmetries, 23, e00203. https://doi.org/10.1016/j.jeca.2021.e00203
KHALFAOUI, R., SHAHZAD, U., GHAEMI ASL, M., & BEN JABEUR, S. (2023). Investigating the spillovers between energy, food, and agricultural commodity markets: New insights from the quantile coherency approach. The Quarterly Review of Economics and Finance, 88, 63–80. https://doi.org/10.1016/j.qref.2022.12.006
LIONTAKIS, A. (2012). Food Price Inflation Rates in the Euro Zone: Distribution Dynamics and Convergence Analysis. Economics Research International, 2012, e868216. https://doi.org/10.1155/2012/868216
LIONTAKIS, A. & KREMMYDAS, D. (2014). Food Inflation in the European Union: Distribution Analysis and Spatial Effects. Geographical Analysis, 46. https://doi.org/10.1111/gean.12033
LIONTAKIS, A. & PAPADAS, C. T. (2010). Stochastic Convergence and Distribution Dynamics of Food Price Inflation Rates in EU. AUA Working Paper Series, No2010-6.
MADDALA, G. S., & WU, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61, 631–652. https://doi.org/10.1111/1468-0084.0610s1631
MAWEJJE, J. (2016). Food prices, energy and climate shocks in Uganda. Agricultural and Food Economics, 4(1), 4. https://doi.org/10.1186/s40100-016-0049-6
ODONGO, M. T., MISATI, R. N., KAMAU, A. W., & KISINGU, K. N. (2022). Climate Change and Inflation in Eastern and Southern Africa. Sustainability, 14(22), 1–17. https://doi.org/10.3390/su142214764
PEDRONI, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61, 653–670. https://doi.org/10.1111/1468-0084.0610s1653
PEERSMAN, G., RÜTH, S. K., & VAN DER VEKEN, W. (2021). The interplay between oil and food commodity prices: Has it changed over time? Journal of International Economics, 133, 103540. https://doi.org/10.1016/j.jinteco.2021.103540
PESARAN, M. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels' IZA Discussion Paper, No. 1240. https://repec.iza.org/dp1240.pdf
PESARAN, M. (2007). A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics. 22. 265-312. https://doi.org/10.1002/jae.951
TULE, M. K., SALISU, A. A., & CHIEMEKE, C. C. (2019). Can agricultural commodity prices predict Nigeria’s inflation? Journal of Commodity Markets, 16, 100087. https://doi.org/10.1016/j.jcomm.2019.02.002
UMAR, U. A., & UMAR, A. (2022). Effects of Exchange Rate on Food Inflation in Nigeria: A Non-Linear ARDL Approach. Gusau International Journal of Management and Social Sciences, 5(1), 195–209. https://www.gijmss.com.ng/index.php/gijmss/article/view/106/88
WESTERLUND, J. (2005). New simple tests for panel cointegration. Econometric Reviews, 24, 297–316. https://doi.org/10.1080/07474930500243019
WESTERLUND, J., & NARAYAN, P. (2015). Testing for predictability in conditionally heteroskedastic stock returns. Journal of Financial Econometrics, 13(2), 342–375. https://doi.org/10.1093/jjfinec/nbu001
WOOLDRIDGE, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press. https://doi.org/10.1007/s00712-003-0589-6
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