More Powerful Portfolio Approaches to Regressing Abnormal Returns on Firm‐Specific Variables for Cross‐Sectional Studies
提出投资组合加权最小二乘和投资组合常相关模型两种回归方法，利用异常收益率的异方差性和相关性，比传统投资组合OLS方法更有效地检验回归系数，适用于金融实证研究。
ABSTRACT OLS regression ignores both heteroscedasticity and cross‐correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A Portfolio OLS (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. We propose Portfolio Weighted Least Squares (PWLS) and Portfolio Constant Correlation Model (PCCM) regressions to improve the power. Both utilize the heteroscedasticity of abnormal returns in estimating the coefficients; PWLS ignores the correlations, while PCCM uses intra‐and inter‐industry correlations. Simulation results show that both lead to more powerful tests of regression coefficients than POLS.