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Measuring Abnormal Performance: The Event Parameter Approach Using Joint Generalized Least Squares

Published online by Cambridge University Press:  06 April 2009

Paul H. Malatesta
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Abstract

Event studies generally seek to measure abnormal security performance associated with firm-specific events. In principle, estimators of and tests for abnormal performance should appropriately reflect cross-sectional dependence between abnormal returns to different securities. Joint generalized least squares provides a natural framework for developing such estimators and tests. This paper derives a joint generalized least squares estimator and related test statistic applicable in the typical event study context. Simulation techniques comparable to those of Brown and Warner [2] are used to assess the frequency distribution of the estimator and power of the test statistic. Several simpler procedures are simulated for comparison. The results provide no evidence that joint generalized least squares is superior to simpler procedures.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 21 , Issue 1 , March 1986 , pp. 27 - 38
Copyright
Copyright © School of Business Administration, University of Washington 1986

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References

[1]Binder, J. “Measuring the Effects of Regulation with Stock Price Data: A New Methodology.” Manuscript, University of Chicago, Chicago, IL (01 1983).Google Scholar
[2]Brown, S., and Warner, J.. “Measuring Security Price Performance.” Journal of Financial Economics, Vol. 8 (09 1980), pp. 205–258.CrossRefGoogle Scholar
[3]Brown, S., and Warner, J.. “Using Daily Stock Returns in Event Studies.” Manuscript, University of Rochester, Rochester, NY (02 1983).Google Scholar
[4]Kakwani, N. “The Unbiasedness of Zellner's Seemingly Unrelated Regressions Equations Estimators.” Journal of the American Statistical Association, Vol. 62 (03 1967), pp. 141–142.CrossRefGoogle Scholar
[5]Schipper, K., and Thompson, R.. “The Impact of Merger-Related Regulations on the Shareholders of Acquiring Firms.” Journal of Accounting Research, Vol. 21 (Spring 1983), pp. 184–221.CrossRefGoogle Scholar
[6]Schipper, K., and Thompson, R.. “The Impact of Merger-Related Regulations: Exact Distributions of Test Statistics and a Reinterpretation of Significance.” Manuscript, University of Chicago, Chicago, IL (04 1984).Google Scholar
[7]Theil, H.Principles of Econometrics. New York: John Wiley and Sons, Inc. (1971).Google Scholar
[8]Thompson, R. “Conditioning the Return Generating Process on Firm Specific Events: A Discussion of Event Study Methods.” Manuscript, University of British Columbia, Vancouver, British Columbia, Canada (08 1983).Google Scholar
[9]Zellner, A. “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias.” Journal of the American Statistical Association, Vol. 57 (06 1962), pp. 348–368.CrossRefGoogle Scholar