An Application of the Bootstrap Method to the Analysis of Squared, Standardized Market Model Prediction Errors
研究信息含量研究中平方标准化市场模型预测误差的显著性检验，发现正态分布理论在尖峰市场数据中存在偏差，并展示如何用自助法修正这一偏差。
This paper examines significance tests on squared, standardized market model prediction errors as performed in some information content studies. It makes two principal points. First, the normal distribution theory for these statistics is biased against the null hypothesis of no information effect when applied to leptokurtic market data. Second, the bias can be corrected using the bootstrap method,' although not in the most obvious way. Patell's [1976] earnings forecast data set is used as an illustration. In section 2, I consider the effect of departures from normality on the distribution theory for standardized market model prediction errors. I show that for large estimation samples and leptokurtic disturbances the normal theory formula for the variance of squared prediction errors underestimates the true variance. This result is the focus of the paper, because it identifies the specific type of departure from normality that affects inferences based on squared prediction errors. Section 3 describes a simulation experiment which demonstrates the empirical relevance of the bias in the normal theory formula for sample