Abstract
The objective of this paper is to examine whether including oil price to the agricultural prices forecasting model can improve the forecasting performance. We employ linear Bayesian vector autoregressive (BVAR) and Markov switching Bayesian vector autoregressive (MS-BVAR) as innovation tools to generate the out-of-sample forecast for the agricultural prices as well as compare the performance of these two forecasting models. The results show that the model which includes the information of oil price and its shock outperforms other models. More importantly, linear model performs well in one- to three-step-ahead forecasting, while Markov switching model presents greater forecasting accuracy in the longer time horizon.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Y., Wu, C., Yang, L.: Oil price shocks and agricultural commodity prices. Energ. Econ. 44, 22–35 (2014)
Saghaian, S.H.: The impact of the oil sector on commodity prices: correlation or causation? J. Agric. Appl. Econ. 42(03), 477–485 (2010)
Esmaeili, A., Shokoohi, Z.: Assessing the effect of oil price on world food prices: application of principal component analysis. Energ. Policy 39(2), 1022–1025 (2011)
Zhang, Z., Lohr, L., Escalante, C., Wetzstein, M.: Food versus fuel: what do prices tell us? Energ. Policy 38(1), 445–451 (2010)
Campiche, J.L., Bryant, H.L., Richardson, J.W., Outlaw, J.L.: Examining the evolving correspondence between petroleum prices and agricultural commodity prices. In: The American Agricultural Economics Association Annual Meeting, Portland, OR (2007)
Gupta, R., Kotz, K.: The role of oil prices in the forecasts of South African interest rates: a Bayesian approach. Energ. Econ. 61, 270–278 (2017)
Pastpipatkul, P., Yamaka, W., Wiboonpongse, A., Sriboonchitta, S.: Spillovers of quantitative easing on financial markets of Thailand, Indonesia, and the Philippines. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 374–388. Springer (2015)
Sims, C.A., Waggoner, D.F., Zha, T.: Methods for inference in large multiple-equation Markov-switching models. J. Econ. 146(2), 255–274 (2008)
Nason, J.M., Tallman, E.W.: Business cycles and financial crises: the roles of credit supply and demand shocks. Macroecon. Dyn. 19(4), 836–882 (2015)
McCracken, M.W.: Asymptotics for out of sample tests of Granger causality. J. Econ. 140(2), 719–752 (2007)
Altavilla, C., De Grauwe, P.: Forecasting and combining competing models of exchange rate determination. Appl. Econ. 42(27), 3455–3480 (2010)
Chen, Q., Hong, Y.: Predictability of equity returns over different time horizons: a nonparametric approach. Manuscript, Cornell University (2010)
Marcellino, M.: A Comparison of Time Series Models for Forecasting GDP Growth and Inflation. Bocconi University, Italia (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Osathanunkul, R., Khiewngamdee, C., Yamaka, W., Sriboonchitta, S. (2018). The Role of Oil Price in the Forecasts of Agricultural Commodity Prices. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-70942-0_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70941-3
Online ISBN: 978-3-319-70942-0
eBook Packages: EngineeringEngineering (R0)