Inference in Long Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings

Abstract

Statistical inference in many long-horizon event studies has been hampered by the fact that abnormal returns are neither normally distributed nor independent. This study presents a new approach to inference that overcomes these difficulties. To illustrate the use of the methodology, long-horizon returns of initial public offerings (IPOs) are examined. Inference using the new procedure is shown to be sensitive to both non-normality and cross-correlation and to dominate other popular testing methods.

JEL Classification: G12, G14