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Long-run performance evaluation: Correlation and heteroskedasticity-consistent tests

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  • Jegadeesh, Narasimhan
  • Karceski, Jason

Abstract

Although there is an extensive literature that evaluates long-run stock returns, the statistical tests that are commonly used are misspecified when event firms share common characteristics. For example, industry clustering or overlapping returns in the sample contribute to test misspecification. We propose a new test of long-run performance that allows for heteroskedasticity and autocorrelation. Our tests are well-specified in random samples and in samples with industry clustering and with overlapping returns.

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  • Jegadeesh, Narasimhan & Karceski, Jason, 2009. "Long-run performance evaluation: Correlation and heteroskedasticity-consistent tests," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 101-111, January.
    • Handle: RePEc:eee:empfin:v:16:y:2009:i:1:p:101-111
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    7. Dutta, Anupam & Knif, Johan & Kolari, James W. & Pynnonen, Seppo, 2018. "A robust and powerful test of abnormal stock returns in long-horizon event studies," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 1-24.
    8. Dionysia Dionysiou, 2015. "Choosing Among Alternative Long-Run Event-Study Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 158-198, February.
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    14. Anupam Dutta & David McMillan, 2015. "Improved calendar time approach for measuring long-run anomalies," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1065948-106, December.
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