Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes

Krzysztof Drachal

doi:10.3390/su11195305

Abstract

In the described research three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed. In particular, one-month ahead forecasts were built with techniques like dynamic model averaging (DMA), the median probability model and Bayesian model averaging. The common features of these methods are time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty. In other words, starting with multiple potentially important explanatory variables, various component linear regression models can be constructed. Then, from these models an averaged forecast can be constructed. Moreover, the mentioned techniques can be easily modified from model averaging into a model selection approach. Considering as benchmark models, time-varying parameters regression with all considered potential price drivers, historical average, ARIMA (Auto-Regressive Integrated Moving Average) and the naïve forecast models, the Diebold–Mariano test suggested that DMA is an interesting alternative model, if forecast accuracy is the aim. Secondly, the interpretation of time-varying weights ascribed to component models containing a given variable suggested that economic development of emerging BRIC economies (Brazil, Russia, India and China) is recently one of the most important drivers of agricultural commodities prices. The analysis was made on the monthly data between 1976 and 2016. The initial price drivers were various fundamental, macroeconomic and financial factors.

Highlights

Forecasting of agricultural commodities is an important theoretical and practical topic
It can be stated that the selected dynamic model averaging (DMA) model with α = 0.99 = λ produced significantly more accurate forecasts than the majority of the alternative models
The presented research was focused on applying some novel Bayesian model combination schemes to forecasting the spot prices of the selected grain commodities

Summary

Introduction

Forecasting of agricultural commodities is an important theoretical and practical topic. Since approximately 2000, it can be observed that there exists a rising trend in agricultural prices This can be clearly seen from analyzing, for example, IMF (International Monetary Fund) commodity food price index, including cereal, vegetable oil, meat, seafood, bananas, sugar and orange prices [1]. These prices are important, because in some developing countries rising food prices or its shortages can lead to riots and increase political and economic uncertainty [2]. These prices have a significant impact on farmers. They are an important component in policymaking and sustainable development [3]

Methods

Results

Conclusion