Forecasting seasonally cointegrated systems: supply response of the Austrian breeding sow herd
研究了在农业供给模型中纳入季节性的重要性，通过奥地利农业数据估计季节性协整向量自回归模型，发现忽略季节性协整的模型在短期预测中表现更好，但季节性协整模型在长期预测中更优。
This paper examines the relevance of incorporating seasonality in agricultural supply models. Former studies have eliminated the problem of seasonality by using seasonally adjusted data. Recent developments in cointegration techniques allow the comprehensive modelling of error correcting structures in the presence of seasonality. We consider a four—Advariable model for Austrian agriculture. Series on the producer price for soybeans, bulls and pigs, as well as the stock of breeding sows are included. A vector autoregression incorporating seasonal cointegration is estimated. A tentative interpretation of long-run and seasonal features is considered. Forecasting experiments are reported. The results of our experiments indicate that models that do not account for seasonal cointegration may yield better forecasts at short prediction horizons, but the seasonally cointegrated model tends to dominate at larger step sizes.