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Journal of Financial Econometrics Vol. 2, No. 3, pp. 451-471
Journal of Financial Econometrics, Vol. 2, No. 3, © Oxford University Press 2004; all rights reserved.

Improving Tests of Abnormal Returns by Bootstrapping the Multivariate Regression Model with Event Parameters

Scott E. Hein
     Texas Tech University

Peter Westfall
     Texas Tech University

Address correspondence to Scott Hein, Jerry S. Rawls College of Business, Texas Tech University, Lubbock, TX 79409-2101, or e-mail: odhen{at}ba.ttu.edu.

Parametric dummy variable-based tests for event studies using multivariate regression are not robust to nonnormality of the residual, even for arbitrarily large sample sizes. Bootstrap alternatives are described, investigated, and compared for cases where there are nonnormalities, and cross-sectional and time-series dependencies. Independent bootstrapping of residual vectors from the multivariate regression model controls type I error rates in the presence of cross-sectional correlation, and surprisingly, even in the presence of time-series dependence structures. The proposed methods not only improve upon parametric methods, but also allow development of new and powerful event study tests for which there is no parametric counterpart.

KEYWORDS: bootstrap, cross-sectional correlation, event parameter estimation, event study, significance level, simulation

Received June 10, 2003; revised February 2, 2004; accepted April 26, 2004


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