COINTEGRATION BETWEEN U.S. WHEAT MARKETS
研究了美国12个内陆市场与休斯顿港口小麦月度价格的协整关系，比较了误差修正模型与向量自回归模型的预测表现，发现使用水平向量自回归模型在长期预测中误差偏差更大但方差更小。
ABSTRACT. Average monthly price data from twelve hinterland markets and the Houston port price for wheat are studied in a cointegration framework using the Engle‐Granger “two‐step” procedure and Johansen's maximum likelihood procedure. Out‐of‐sample forecasts from an error correction model are compared to those from a vector autoregression fit to levels and a univariate autoregression fit to first differences. This comparison suggests that modeling these (cointegrated) data as a levels vector autoregression, rather than as an error‐correction process, results in significantly higher error bias, but lower error variance, at long horizons.