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

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农产品价格动态及其决定因素：综合计量经济学方法

我们提出了一个综合建模框架，旨在量化农产品价格对其潜在决定因素变化的反应。根据小麦、大豆和玉米价格的短期样本外预测（提前 1 到 12 个月）的质量，通过专注于规范选择来明确评估模型不确定性问题。单变量和多变量自回归模型（自回归 [AR]、向量自回归 [VAR] 和向量误差校正 [VEC] 规范，使用频率论和贝叶斯方法估计）、异方差规范（AR 条件异方差 [ARCH] 和广义 AR 条件异方差 [ GARCH] 模型）以及这些模型的组合，包括有关市场基本面的信息，宏观经济和金融发展以及气候变量。此外，我们评估了商业周期中商品价格动态的潜在非线性。我们的结果表明，衡量市场基本面和宏观经济发展（以及在较小程度上，金融发展）的变量包含用于商品价格样本外预测的系统预测信息，并且农产品价格对国际竞争力的冲击反应强烈，如以实际汇率的变化来衡量。