Ordinary Least Squares regression ignores both heteroscedasticity and cross-correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A portfolio ordinary least squares (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. The authors 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.

Abstract Reviews the book "Specifying and Diagnostically Testing Econometric Models," by Houston H. Stokes.

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.

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.

We extend Chandra and Rohrbach (1990) to explain how to develop a longitudinal rank test ( r ‐test) analogous to any t ‐test used in the event study literature. We compare all analogous pairs using market model residuals. The r ‐test is more powerful than the t ‐test in each pair. This suggests that if the researcher intends to use any t ‐test then, for more power, the comparable test should be preferred. These results should be useful to the researcher in selecting an r ‐test for event study because now the same flexibility of choosing an r ‐test as a t ‐test is available. Résumé. Les auteurs poussent plus loin les travaux de Chandra et Rohrbach (1990) pour expliquer comment mettre au point un test de rangs logitudinaux (test r ) analogue aux différents tests t utilisés dans les ouvrages portant sur l'étude d'événements. Ils comparent toutes les paires analogues en utilisant les résiduels des modèles de marché. Le test r est plus puissant que le test t dans chacune des paires, de sorte qu'on peut penser que si le chercheur prévoit utiliser un test t pour sa puissance, il aurait avantage à recourir au test r comparable. Ces résultats devraient être utiles aux chercheurs dans la sélection d'un test r pour l'étude d'événements puisque, dorénavant, le choix d'un test r peut offrir la même souplesse que celui d'un test t