Short-horizon event study estimation with a STAR model and real contaminated events
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We propose a test statistic for nonzero mean abnormal returns based on a Smooth Transition Auto Regressive (STAR) model specification. Estimation of STAR takes into account the probability of contaminated events that could otherwise bias the parameters of the market model and thus the specification and power of the test statistic. Using both simulated and real stock returns data from mergers and acquisitions, we find that the STAR test statistic is robust to contaminated events occurring in the estimation window and in the presence of event-induced increase in return variance. Under the STAR test statistic the true null hypothesis is rejected at appropriate levels. Moreover, it exhibits the highest levels of power when compared with other test statistics that are widely and routinely applied in short-horizon event studies.
KeywordsEvent studies Test statistics Contaminated events Markov switching regression model Smooth Transition Auto Regressive model
JEL ClassificationG14 G34
The authors would like to thank Milto Hadjikyriakou for providing excellent research assistance. The authors also acknowledge comments and constructive suggestions from participants at the 5th CSDA International Conference on Computational and Financial Econometrics (December 2011, UK).
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