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

Öz

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.

Anahtar Kelimeler

• Ham Petrol Fiyatları
• Gıda Fiyatları
• Vektör Hata Düzeltme Modeli
• Granger Nedensellik
• Yapısal Kırılmalar

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

Öz

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.

Anahtar Kelimeler

• Crude Oil Prices
• Food Prices
• Vector Error Correction Model
• Granger Causality
• Structural Breaks

Kaynakça

1. 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
2. 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
3. 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
4. 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
5. Enders, W. (2015). Applied econometric time series (4th ed.). Wiley.
6. 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
7. 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
8. 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

9. Granger, C. W. J., & Newbold, P. (2016). Forecasting economic time series (3rd ed.). Academic Press.
10. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill Education.
11. Hamilton, J. D. (2020). Time series analysis. Princeton University Press.
12. 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
13. 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
14. 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
15. Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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