A Robust and Powerful Test of Abnormal Stock Returns in Long-Horizon Event Studies

Based on Dutta et al. (2018) , this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) . Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss.


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install.packages("crseEventStudy")

1.1 by Siegfried Köstlmeier, a month ago


https://github.com/skoestlmeier/crseEventStudy


Browse source code at https://github.com/cran/crseEventStudy


Authors: Siegfried Köstlmeier [aut, cre] , Seppo Pynnonen [aut]


Documentation:   PDF Manual  


Task views: Empirical Finance


BSD_3_clause + file LICENSE license


Imports methods, stats, sandwich

Suggests testthat


See at CRAN