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dc.contributor.authorRossarin Osathanunkulen_US
dc.contributor.authorChatchai Khiewngamdeeen_US
dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.description.abstract© Springer International Publishing AG 2018. 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.en_US
dc.subjectComputer Scienceen_US
dc.titleThe role of oil price in the forecasts of agricultural commodity pricesen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume753en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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