Description: Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>).

Description: Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.

Title: A Robust and Powerful Test of Abnormal Stock Returns in
Authors@R: c(person(given = "Siegfried", family = "Köstlmeier", role = c("aut", "cre"), email = "siegfried.koestlmeier@gmail.com"), person(given = "Seppo", family = "Pynnonen", role = c("aut"), email = "sjp@uwasa.fi"))
Description: 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.
Packaged: 2018-11-05 15:00:26 UTC; Siegfried
Author: Siegfried Köstlmeier [aut, cre],