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Ekonomik Şoklar Altında Petrol-Gıda Fiyat Dinamikleri: Yapısal Bir VECM Yaklaşımı

Year 2025, Volume: 9 Issue: 3, 1293 - 1305, 19.09.2025
https://doi.org/10.30586/pek.1684225

Abstract

Bu çalışma, Ocak 1990 ile Ağustos 2012 dönemine ait aylık zaman serisi verilerini kullanarak, küresel ham petrol fiyatları ile gıda fiyatları arasındaki dinamik ilişkiyi Yapısal Vektör Hata Düzeltme Modeli (VECM) kapsamında incelemektedir. FAO Gıda Fiyat Endeksi ile IMF Ham Petrol Fiyat Endeksi veri seti olarak kullanılmış; serilerin durağanlığı ADF, PP, KPSS ve Zivot-Andrews birim kök testleriyle analiz edilmiştir. Bilgi kriterlerine göre optimal gecikme uzunluğu belirlendikten sonra Johansen eşbütünleşme testi uygulanmış ve uzun dönemli eşbütünleşme ilişkisi tespit edilememiştir. VECM bulguları, petrol fiyatlarındaki %1’lik artışın gıda fiyatlarını uzun vadede %0,506 oranında artırdığını, kısa vadede ise gıda fiyatlarının petrol fiyatları üzerinde Granger nedenselliği taşıdığını göstermektedir. Bulgular; enerji maliyetleri, biyoyakıt talebi ve lojistik giderler yoluyla oluşan çift yönlü geçişkenliğe işaret etmektedir. Bu çalışma, gelişmekte olan ülkelerde fiyat istikrarı ve gıda güvenliği hedefleri doğrultusunda entegre piyasa izleme sistemleri, stratejik rezerv mekanizmaları ve esnek enflasyon hedeflemesi gibi makroekonomik politika araçlarının birlikte uygulanmasının önemine dikkat çekmektedir. Ayrıca, gelecekteki araştırmalar için genişletilmiş veri kapsamı ve doğrusal olmayan modellerle analiz önerilmektedir.

References

  • Adeosun, O. A., Olayeni, R. O., & Tabash, M. I. (2023). Revisiting the oil and food prices dynamics: A time-varying approach. Journal of Business Cycle Research, 19, 275–309. https://doi.org/10.1007/s41549-023-00083-3
  • Alom, F., Ward, B. D., & Hu, B. (2013). Macroeconomic effects of world oil and food price shocks in Asia and Pacific economies: Application of SVAR models. OPEC Energy Review, 37(3), 327–372. https://doi.org/10.1111/opec.12015
  • Apergis, N., & Rezitis, A. N. (2011). Food price volatility and macroeconomic determinants: Evidence from GARCH and VAR models. Applied Economics, 43(29), 4133–4140. https://doi.org/10.1080/00036841003724401
  • Campiche, J. L., Bryant, H. L., Richardson, J. W., & Outlaw, J. L. (2007). Examining the evolving correspondence between petroleum prices and agricultural commodity prices. Journal of Agricultural and Applied Economics, 39(1), 71–84. https://doi.org/10.1017/S1074070800022823
  • Enders, W. (2015). Applied econometric time series (4th ed.). Wiley.
  • Engle, R. F., & Granger, C. W. J. (1987). Cointegration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236
  • Fasanya, I., & Akinbowale, S. (2019). Modelling the return and volatility spillovers of crude oil and food prices in Nigeria. Energy, 169, 186–205. https://doi.org/10.1016/j.energy.2018.12.011
  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791
  • Granger, C. W. J., & Newbold, P. (2016). Forecasting economic time series (3rd ed.). Academic Press.
  • Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill Education.
  • Hamilton, J. D. (2020). Time series analysis. Princeton University Press.
  • Harri, A., Nalley, L., & Hudson, D. (2009). The relationship between oil, exchange rates, and commodity prices. Journal of Agricultural and Applied Economics, 41(2), 501–510. https://doi.org/10.1017/S1074070800002902
  • Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3
  • Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
  • Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.
  • Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 1098–1104. https://doi.org/10.1016/j.eneco.2011.09.008
  • Nazlioglu, S., Soytas, U., & Gupta, R. (2021). Dynamic spillovers between oil and food prices in emerging economies. Energy Economics, 95, 105125. https://doi.org/10.1016/j.eneco.2021.105125
  • Pal, D., & Mitra, S. K. (2018). Interdependence between crude oil and world food prices: A detrended cross-correlation analysis. Physica A: Statistical Mechanics and Its Applications, 492, 1032–1044. https://doi.org/10.1016/j.physa.2017.11.120
  • Paris, A. (2018). On the link between oil and agricultural commodity prices: Do biofuels matter? International Economics, 155, 75–88. https://doi.org/10.1016/j.inteco.2017.12.003
  • Mokni, K., & Ben-Salha, O. (2020). Asymmetric causality in quantiles analysis of the oil-food nexus since the 1960s. Resources Policy, 69, 101874. https://doi.org/10.1016/j.resourpol.2020.101874
  • Nwoko, S. I., & Kaulu, B. (2021). Effects of crude oil prices on copper and maize prices. Future Business Journal, 7(1), 54. https://doi.org/10.1186/s43093-021-00100-w
  • Reboredo, J. C. (2012). Do food and oil prices co-move? Energy Policy, 49, 456–467. https://doi.org/10.1016/j.enpol.2012.06.024
  • Roman, M., & Bilan, Y. (2021). On the relation between global food and crude oil prices. Economic Modelling, 94, 1–9. https://doi.org/10.1016/j.econmod.2020.09.011
  • Shin, Y. (1994). A residual-based test of the null of cointegration against the alternative of no cointegration. Econometric Theory, 10(1), 91–115. https://doi.org/10.1017/S0266466600008234
  • Tiwari, A. K., Khalfaoui, R., Solarin, S. A., & Shahbaz, M. (2018). Analyzing the time-frequency lead–lag relationship between oil and agricultural commodities. Energy Economics, 76, 470–494. https://doi.org/10.1016/j.eneco.2018.10.005
  • Vu, T. N., Vo, D. H., Ho, C. M., & Van, L. H. (2019). Modeling the impact of agricultural shocks on oil price in the US: A new approach. Journal of Risk and Financial Management, 12(3), 147. https://doi.org/10.3390/jrfm12030147
  • Yip, P. S., Brooks, R., Doc, H. X., & Nguyen, D. K. (2020). Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis, 69, 101465. https://doi.org/10.1016/j.irfa.2020.101465
  • Zhang, Z., Lohr, L., & Wetzstein, M. (2010). Food versus fuel: What do prices tell us? Energy Policy, 38(1), 445–451. https://doi.org/10.1016/j.enpol.2009.09.004
  • Zilberman, D., Hochman, G., Rajagopal, D., Sexton, S., & Timilsina, G. R. (2013). The impact of biofuels on commodity food prices: Assessment of findings. American Journal of Agricultural Economics, 95(2), 275–281. https://doi.org/10.1093/ajae/aas037
  • Zivkov, D., Manic, S., & Duraskovic, J. (2020). Short and long-term volatility transmission from oil to agricultural commodities—The robust quantile regression approach. Borsa Istanbul Review, 20(S1), S11–S25. https://doi.org/10.1016/j.bir.2020.06.001
  • Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the Great Crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. https://doi.org/10.1080/07350015.1992.10509904

Oil-Food Price Dynamics under Economic Shocks: A Structural VECM Approach

Year 2025, Volume: 9 Issue: 3, 1293 - 1305, 19.09.2025
https://doi.org/10.30586/pek.1684225

Abstract

This study investigates the dynamic relationship between global crude oil prices and food prices using monthly time series data from January 1990 to August 2012 within the framework of a Structural Vector Error Correction Model (VECM). The FAO Food Price Index and the IMF Crude Oil Price Index are employed as the primary data sources. The stationarity of the series is tested using ADF, PP, KPSS, and Zivot-Andrews unit root tests. After determining the optimal lag length through information criteria, the Johansen cointegration test is applied, but no long-run cointegrating vector is identified. Nevertheless, VECM results indicate that a 1% increase in oil prices leads to a 0.506% rise in food prices in the long run, while food prices have a Granger-causality effect on oil prices in the short run. These findings highlight a bidirectional transmission mechanism through energy costs, biofuel demand, and logistics expenses. The study emphasizes the critical role of coordinated policy tools such as flexible inflation targeting, strategic reserve mechanisms, and integrated market surveillance systems in ensuring food security and price stability in developing economies. Future research should consider extended datasets and nonlinear modeling approaches to capture regime-dependent dynamics more effectively.

References

  • Adeosun, O. A., Olayeni, R. O., & Tabash, M. I. (2023). Revisiting the oil and food prices dynamics: A time-varying approach. Journal of Business Cycle Research, 19, 275–309. https://doi.org/10.1007/s41549-023-00083-3
  • Alom, F., Ward, B. D., & Hu, B. (2013). Macroeconomic effects of world oil and food price shocks in Asia and Pacific economies: Application of SVAR models. OPEC Energy Review, 37(3), 327–372. https://doi.org/10.1111/opec.12015
  • Apergis, N., & Rezitis, A. N. (2011). Food price volatility and macroeconomic determinants: Evidence from GARCH and VAR models. Applied Economics, 43(29), 4133–4140. https://doi.org/10.1080/00036841003724401
  • Campiche, J. L., Bryant, H. L., Richardson, J. W., & Outlaw, J. L. (2007). Examining the evolving correspondence between petroleum prices and agricultural commodity prices. Journal of Agricultural and Applied Economics, 39(1), 71–84. https://doi.org/10.1017/S1074070800022823
  • Enders, W. (2015). Applied econometric time series (4th ed.). Wiley.
  • Engle, R. F., & Granger, C. W. J. (1987). Cointegration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236
  • Fasanya, I., & Akinbowale, S. (2019). Modelling the return and volatility spillovers of crude oil and food prices in Nigeria. Energy, 169, 186–205. https://doi.org/10.1016/j.energy.2018.12.011
  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791
  • Granger, C. W. J., & Newbold, P. (2016). Forecasting economic time series (3rd ed.). Academic Press.
  • Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill Education.
  • Hamilton, J. D. (2020). Time series analysis. Princeton University Press.
  • Harri, A., Nalley, L., & Hudson, D. (2009). The relationship between oil, exchange rates, and commodity prices. Journal of Agricultural and Applied Economics, 41(2), 501–510. https://doi.org/10.1017/S1074070800002902
  • Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3
  • Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
  • Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.
  • Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 1098–1104. https://doi.org/10.1016/j.eneco.2011.09.008
  • Nazlioglu, S., Soytas, U., & Gupta, R. (2021). Dynamic spillovers between oil and food prices in emerging economies. Energy Economics, 95, 105125. https://doi.org/10.1016/j.eneco.2021.105125
  • Pal, D., & Mitra, S. K. (2018). Interdependence between crude oil and world food prices: A detrended cross-correlation analysis. Physica A: Statistical Mechanics and Its Applications, 492, 1032–1044. https://doi.org/10.1016/j.physa.2017.11.120
  • Paris, A. (2018). On the link between oil and agricultural commodity prices: Do biofuels matter? International Economics, 155, 75–88. https://doi.org/10.1016/j.inteco.2017.12.003
  • Mokni, K., & Ben-Salha, O. (2020). Asymmetric causality in quantiles analysis of the oil-food nexus since the 1960s. Resources Policy, 69, 101874. https://doi.org/10.1016/j.resourpol.2020.101874
  • Nwoko, S. I., & Kaulu, B. (2021). Effects of crude oil prices on copper and maize prices. Future Business Journal, 7(1), 54. https://doi.org/10.1186/s43093-021-00100-w
  • Reboredo, J. C. (2012). Do food and oil prices co-move? Energy Policy, 49, 456–467. https://doi.org/10.1016/j.enpol.2012.06.024
  • Roman, M., & Bilan, Y. (2021). On the relation between global food and crude oil prices. Economic Modelling, 94, 1–9. https://doi.org/10.1016/j.econmod.2020.09.011
  • Shin, Y. (1994). A residual-based test of the null of cointegration against the alternative of no cointegration. Econometric Theory, 10(1), 91–115. https://doi.org/10.1017/S0266466600008234
  • Tiwari, A. K., Khalfaoui, R., Solarin, S. A., & Shahbaz, M. (2018). Analyzing the time-frequency lead–lag relationship between oil and agricultural commodities. Energy Economics, 76, 470–494. https://doi.org/10.1016/j.eneco.2018.10.005
  • Vu, T. N., Vo, D. H., Ho, C. M., & Van, L. H. (2019). Modeling the impact of agricultural shocks on oil price in the US: A new approach. Journal of Risk and Financial Management, 12(3), 147. https://doi.org/10.3390/jrfm12030147
  • Yip, P. S., Brooks, R., Doc, H. X., & Nguyen, D. K. (2020). Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis, 69, 101465. https://doi.org/10.1016/j.irfa.2020.101465
  • Zhang, Z., Lohr, L., & Wetzstein, M. (2010). Food versus fuel: What do prices tell us? Energy Policy, 38(1), 445–451. https://doi.org/10.1016/j.enpol.2009.09.004
  • Zilberman, D., Hochman, G., Rajagopal, D., Sexton, S., & Timilsina, G. R. (2013). The impact of biofuels on commodity food prices: Assessment of findings. American Journal of Agricultural Economics, 95(2), 275–281. https://doi.org/10.1093/ajae/aas037
  • Zivkov, D., Manic, S., & Duraskovic, J. (2020). Short and long-term volatility transmission from oil to agricultural commodities—The robust quantile regression approach. Borsa Istanbul Review, 20(S1), S11–S25. https://doi.org/10.1016/j.bir.2020.06.001
  • Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the Great Crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. https://doi.org/10.1080/07350015.1992.10509904
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Cyclical Fluctuations
Journal Section Makaleler
Authors

Orkun Bayram 0000-0001-9958-7822

Early Pub Date September 13, 2025
Publication Date September 19, 2025
Submission Date April 25, 2025
Acceptance Date June 10, 2025
Published in Issue Year 2025 Volume: 9 Issue: 3

Cite

APA Bayram, O. (2025). Ekonomik Şoklar Altında Petrol-Gıda Fiyat Dinamikleri: Yapısal Bir VECM Yaklaşımı. Politik Ekonomik Kuram, 9(3), 1293-1305. https://doi.org/10.30586/pek.1684225

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