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

Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, 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) <doi:10.1016/B978-0-444-53265-7.50015-9>. 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.

Version:	1.2
Depends:	R (≥ 3.5)
Imports:	methods, stats, sandwich
Suggests:	testthat
Published:	2019-08-20
Author:	Siegfried Köstlmeier [aut, cre], Seppo Pynnonen [aut]
Maintainer:	Siegfried Köstlmeier <siegfried.koestlmeier at gmail.com>
License:	BSD_3_clause + file LICENSE
URL:	https://github.com/skoestlmeier/crseEventStudy
NeedsCompilation:	no
In views:	Finance
CRAN checks:	crseEventStudy results

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