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) 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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1.2.1 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