THE CAUSALITY BETWEEN AGRICULTURAL RAW MATERIALS AND ECONOMIC POLICY UNCERTAINTY: EVIDENCE FROM THE TIME-VARYING GRANGER CAUSALITY

Abstract

This paper aims to examine the time-varying dynamics of the causality interaction between agricultural raw materials commodity prices and the US economic policy uncertainty (EPU). To this end, we use monthly data for the period spanning from January 1992 to November 2021. We employ a time-varying Granger causality test to provide empirical evidence about the time-varying dynamics of the causality interaction and thereby revealing potential heterogeneities of these interactions during major historical events. The results show that causality running from EPU to agricultural raw materials, as well as causality running from agricultural raw materials to EPU, exhibits time-varying dynamics. More specifically, the findings reveal that causality generally tended to run from agricultural raw materials to EPU for most of the 2000-2014 period, but reversed with the US-China trade war and the Covid-19 pandemic period. This result highlights the importance of modeling the potential causality interactions in the economic uncertainty-commodity prices nexus within a dynamic framework and implies that these interactions cannot be considered independently of the prevailing economic, political and global conditions.

Keywords

• Economic policy uncertainty
• Agricultural raw materials
• Time-varying granger causality
• Covid-19

TARIMSAL HAMMADDELER VE EKONOMİ POLİTİKASI BELİRSİZLİĞİ NEDENSELLİK İLİŞKİSİ: ZAMANLA DEĞİŞEN GRANGER NEDENSELLIK TESTİNDEN KANITLAR

Öz

Bu çalışma, tarımsal hammadde emtia fiyatları ile ABD ekonomik politikası belirsizliği (EPU) arasındaki nedensellik etkileşiminin zamanla değişen dinamiklerini incelemeyi amaçlamaktadır. Bu amaçla, Ocak 1992 - Kasım 2021 dönemini kapsayan aylık veriler kullanılmıştır. Nedensellik etkileşiminin zamanla değişen dinamikleri hakkında ampirik kanıtlar sağlamak ve böylece önemli tarihsel olaylar sırasında bu etkileşimlerin potansiyel heterojenliklerini ortaya çıkarmak için zamanla değişen bir Granger nedensellik testi kullanıyoruz. Sonuçlar, EPU'dan tarımsal hammaddelere uzanan nedenselliğin yanı sıra tarımsal hammaddelerden EPU'ya uzanan nedenselliğin de zamanla değişen dinamikler sergilediğini göstermektedir. Daha spesifik olarak, bulgular 2000-2014 döneminin büyük bir bölümünde nedenselliğin genellikle tarımsal hammaddelerden EPU'ya doğru gitme eğiliminde olduğunu, ancak bu durumun ABD-Çin ticaret savaşı ve Covid-19 pandemi dönemi ile tersine döndüğünü ortaya koymaktadır. Bu sonuç, ekonomik belirsizlik-emtia fiyatları ilişkisindeki potansiyel nedensellik etkileşimlerinin dinamik bir çerçeve içinde modellenmesinin önemini vurgulamakta ve bu etkileşimlerin hakim ekonomik, politik ve küresel koşullardan bağımsız olarak düşünülemeyeceğini ima etmektedir.

Anahtar Kelimeler

• Ekonomi politika belirsizliği
• Tarımsal hammaddeler
• Zamanla değişen Granger nedensellik
• Covid-19

Kaynakça

1. Ahmed, M. Y., & Sarkodie, S. A. (2021). COVID-19 pandemic and economic policy uncertainty regimes affect commodity market volatility. Resources Policy, 74 (April), 102303. https://doi.org/10.1016/j.resourpol.2021.102303
2. Antonakakis, N., Chatziantoniou, I., & Filis, G. (2014). Dynamic spillovers of oil price shocks and economic policy uncertainty. Energy Economics, 44, 433–447. https://doi.org/10.1016/j.eneco.2014.05.007
3. Apergis, N., Hayat, T., & Saeed, T. (2021). US partisan conflict uncertainty and oil prices. Energy Policy, 150 (January), 112118. https://doi.org/10.1016/j.enpol.2020.112118
4. Bakas, D., & Triantafyllou, A. (2018). The impact of uncertainty shocks on the volatility of commodity prices. Journal of International Money and Finance, 87, 96–111. https://doi.org/10.1016/j.jimonfin.2018.06.001
5. Bakas, D., & Triantafyllou, A. (2020). Commodity price volatility and the economic uncertainty of pandemics. Economics Letters, 193, 109283. https://doi.org/10.1016/j.econlet.2020.109283
6. Baker, S. R., & Bloom, N. (2013). Does uncertainty reduce growth? Using disasters as natural experiments. NBER Working Papers, 1–31. http://www.nber.org/papers/w19475
7. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
8. Baker, S.R., Bloom, N., Davis, S.J., Terry, S.J., (2020). Covid-induced Economic Uncertainty. (0898-2937).

9. Bannigidadmath, D., & Narayan, P. K. (2021). Commodity futures returns and policy uncertainty. International Review of Economics and Finance, 72 (December), 364–383. https://doi.org/10.1016/j.iref.2020.11.009
10. Barsky, R. B., & Kilian, L. (2004). Oil and the macroeconomy Since the 1970s. Journal of Economic Perspectives, 18(4), 115–134. https://doi.org/10.1257/0895330042632708
11. Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. https://doi.org/10.3982/ECTA6248 Bloom, N. (2014). Fluctuations in uncertainty. https://doi.org/10.2139/ssrn.2423515
12. Bloom, N. (2016). Fluctuations in uncertainty. Voprosy Ekonomiki, 2016(4), 30–55. https://doi.org/10.32609/0042-8736-2016-4-30-55
13. Bouri, E., Cepni, O., Gabauer, D., & Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International Review of Financial Analysis, 73(May 2020), 101646. https://doi.org/10.1016/j.irfa.2020.101646
14. Clemente, J., Montañés, A., & Reyes, M. (1998). Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), 175–182. https://doi.org/10.1016/s0165-1765(98)00052-4
15. Garner, A. (2017). Commodity Prices: Policy Target or Information Variable?: Note Author ( s ): C . Alan Garner Source: Journal of Money, Credit and Banking, Vol. 21, No. 4 (Nov., 1989), pp. 508-514 Published by: Ohio State University Press Stable URL: http://. 21(4), 508–514.
16. Hailemariam, A., Smyth, R., & Zhang, X. (2019). Oil prices and economic policy uncertainty: Evidence from a nonparametric panel data model. Energy Economics, 83, 40–51. https://doi.org/10.1016/j.eneco.2019.06.010
17. Huang, J., Li, Y., Zhang, H., & Chen, J. (2021). The effects of uncertainty measures on commodity prices from a time-varying perspective. International Review of Economics and Finance, 71(February 2020), 100–114. https://doi.org/10.1016/j.iref.2020.09.001
18. IMF (2022). International Monetary Fund. IMF Primary Commodity Prices. https://www.imf.org/en/Research/commodity-prices
19. Joëts, M., Mignon, V., & Razafindrabe, T. (2017). Does the volatility of commodity prices reflect macroeconomic uncertainty? Energy Economics, 68, 313–326. https://doi.org/10.1016/j.eneco.2017.09.017
20. Just, M., & Echaust, K. (2022). Dynamic spillover transmission in agricultural commodity markets: What has changed after the COVID-19 threat? Economics Letters, 217, 110671. https://doi.org/10.1016/j.econlet.2022.110671
21. Kaldor, N. (1987). The role of commodity prices in economic recovery. World Development, 15(5), 551–558. https://doi.org/10.1016/0305-750X(87)90002-7
22. Kang, W., & Ratti, R. A. (2013a). Structural oil price shocks and policy uncertainty. Economic Modelling, 35, 314–19. https://doi.org/10.1016/j.econmod.2013.07.025.
23. Kang, W., & Ratti, R. A. (2013b). Oil shocks, policy uncertainty and stock market return. Journal of International Financial Markets, Institutions and Money, 26(1), 305–18. https://doi.org/10.1016/j.intfin.2013.07.001.
24. Kilian, L., 2009. Not All Oil price shocks are alike: disentangling demand and supply shocks. American Economic Review, 99, 1053–1069. https://www.jstor.org/stable/25592494.
25. Leduc, S., & Liu, Z. (2020). The Uncertainty Channel of the Coronavirus. Federal Reserve Bank of San Francisco.
26. Newey, W.K., West, K.D., 1987. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica. 55, 703–708. https://doi.org/10.3386/t0055.
27. Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70(April), 101496. https://doi.org/10.1016/j.irfa.2020.101496
28. Shi, S., Hurn, S., & Phillips, P. C. B. (2020). Causal change detection in possibly integrated systems: Revisiting the money-income relationship. Journal of Financial Econometrics, 18(1), 158–180. https://doi.org/10.1093/JJFINEC/NBZ004
29. Shi, S., Phillips, P. C. B., & Hurn, S. (2018). Change detection and the causal impact of the yield curve. Journal of Time Series Analysis, 39(6), 966–987. https://doi.org/10.1111/jtsa.12427
30. Shi, X., & Shen, Y. (2021). Macroeconomic uncertainty and natural gas prices: Revisiting the Asian Premium. Energy Economics, 94, 105081. https://doi.org/10.1016/j.eneco.2020.105081
31. Stock, J. H., & Watson, M. W. (2012). Disentangling the channels of the 2007-09 recession. Brookings Papers on Economic Activity, 1, 81–135. https://doi.org/10.1353/eca.2012.0005
32. Sun, T. T., Su, C. W., Mirza, N., & Umar, M. (2021). How does trade policy uncertainty affect agriculture commodity prices?. Pacific Basin Finance Journal, 66(February), 101514. https://doi.org/10.1016/j.pacfin.2021.101514
33. Sun, X., Chen, X., Wang, J., & Li, J. (2020). Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains. North American Journal of Economics and Finance, 51(September), 1–15. https://doi.org/10.1016/j.najef.2018.10.002.
34. Tunc, A., Kocoglu, M., & Aslan, A. (2022). Time-varying characteristics of the simultaneous interactions between economic uncertainty, international oil prices and GDP: A novel approach for Germany. Resources Policy, 77(April), 102658. https://doi.org/10.1016/j.resourpol.2022.102658
35. Udmale, P., Pal, I., Szabo, S., Pramanik, M., & Large, A. (2020). Global food security in the context of COVID-19: A scenario-based exploratory analysis. Progress in Disaster Science, 7, 100120. https://doi.org/10.1016/j.pdisas.2020.100120
36. USDA, (2021). Has global agricultural trade been resilient under coronavirus (COVID-19)? findings from an econometric assessment Shawn Arita , Jason Grant , Sharon Sydow , and Jayson Beckman OCE Working Paper Preliminary U . S . Department of Agriculture Office of th. 1–38.
37. Wang, Yudong, Zhang, B., Diao, X., & Wu, C. (2015). Commodity price changes and the predictability of economic policy uncertainty. Economics Letters, 127, 39–42. https://doi.org/10.1016/j.econlet.2014.12.030
38. World Bank (2018). Global economic prospects: Broad-based upturn, but for how long? https://openknowledge.worldbank.org/bitstream/handle/10986/28932/9781464811630.pdf?sequence=16&isAllowed=y
39. Yakubu, M., Sarkodie, S.A., 2021. How COVID-19 pandemic may hamper sustainable economic development. Journal of Public Affairs. Article PA2675 https://doi.org/10.1002/PA.2675
40. Yan, W., Cai, Y., Lin, F., & Ambaw, D. T. (2021). The ımpacts of trade restrictions on world agricultural price volatility during the COVID-19 pandemic. China and World Economy, 29(6), 139–158. https://doi.org/10.1111/cwe.12398
41. Yin, L., & Han, L. (2014). Macroeconomic uncertainty: Does it matter for commodity prices? Applied Economics Letters, 21(10), 711–716. https://doi.org/10.1080/13504851.2014.887181