A Robust and Powerful Test of Abnormal Stock Returns in Long-Horizon Event Studies R package crseEventStudy version 1.2.2 CRAN Task View: AnomalyDetection (https://CRAN.R-project.org/view=AnomalyDetection) CRAN Task View: Finance (https://CRAN.R-project.org/view=Finance) 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) <doi: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. Software Depends: R (>= 3.5) Imports: methods, stats, sandwich Suggests: testthat Siegfried Köstlmeier <siegfried.koestlmeier@gmail.com> Comprehensive R Archive Network (CRAN) Siegfried Köstlmeier [aut, cre] (ORCID: <https://orcid.org/0000-0002-7221-6981>), Seppo Pynnonen [aut] BSD_3_clause + file LICENSE (https://CRAN.R-project.org/package=crseEventStudy/LICENSE) 2022-02-23 application/tgz https://CRAN.R-project.org/package=crseEventStudy doi:10.32614/CRAN.package.crseEventStudy