Trading‐Day Variation: Theory and Implications for Monthly Meat Demand
研究含交易日变化数据的回归模型拟合问题，提出乘法和加法调整方法，发现月度数据中交易日变化对最小二乘估计的偏差较大，而季度数据偏差较小，对肉类需求分析有参考价值。
Abstract We consider the problem of fitting regression models with data containing trading‐day variation. Multiplicative and additive expressions for trading‐day variation are presented. Multiplicative adjustment is more reasonable than the additive but is also more complex. Expressions are derived for biases which trading‐day variation introduces into least squares estimators. Estimated retail demands for beef, pork, and chicken show the biases are large and in the directions predicted when monthly data are used, but are small when quarterly data are used. Multiplicative adjustment is statistically superior to additive adjustment, although practical differences are small.