Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach

We present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem model uncertainty is assessed explicitly by concentrating on specification selection based quality short-term out-of-sample forecasts (1 12 months ahead) for price wheat, soybeans and corn. Univariate multivariate autoregressive models (autoregressive [AR], vector [VAR] error correction [VEC] specifications, estimated using frequentist Bayesian methods), specifications with heteroskedastic errors (AR conditional [ARCH] generalized AR [GARCH] models) combinations these are entertained, including information about market fundamentals, macroeconomic financial developments, climatic variables. In addition, we assess non-linearities dynamics along business cycle. Our results indicate that variables measuring fundamentals developments (and, lesser extent, developments) contain systematic predictive forecasting react robustly shocks international competitiveness, as measured real exchange rate.