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.


Reference manual

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1.2 by Siegfried Köstlmeier, a year ago


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