Package: crseEventStudy 1.2.2

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) <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.

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

crseEventStudy_1.2.2.tar.gz
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crseEventStudy_1.2.2.tgz(r-4.4-any)crseEventStudy_1.2.2.tgz(r-4.3-any)
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crseEventStudy.pdf |crseEventStudy.html
crseEventStudy/json (API)

# Install 'crseEventStudy' in R:
install.packages('crseEventStudy', repos = c('https://skoestlmeier.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/skoestlmeier/crseeventstudy/issues

Datasets:

On CRAN:

empirical-researchevent-studyfinancefinancial-analysis

3.15 score 2 stars 14 scripts 301 downloads 3 exports 3 dependencies

Last updated 3 years agofrom:aa6e660837. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-winOKNov 22 2024
R-4.5-linuxOKNov 22 2024
R-4.4-winOKNov 22 2024
R-4.4-macOKNov 22 2024
R-4.3-winOKNov 22 2024
R-4.3-macOKNov 22 2024

Exports:asrcrseEventsar

Dependencies:latticesandwichzoo