On the Efficiency of Least Squares Regression with Security Abnormal Returns as the Dependent Variable

Imre Karafiath

Journal of Financial and Quantitative Analysis, 1994, vol. 29, issue 2, 279-300

Abstract: Monte Carlo procedures are used to compare the finite sample performance of several estimators that may be used in cross-sectional regressions with security abnormal returns as the dependent variable. Alternative models of event-induced increases in stock return variance are examined for the “event-clustering” scenario. Event clustering implies crosssectional correlation and heteroskedasticity in market model prediction errors, violating one of the fundamental ordinary least squares (OLS) assumptions (i.i.d. disturbances). Nonetheless, provided that the conditions for asymptotic validity derived by Greenwald (1983) are met, the OLS estimator is well specified in finite samples. Further, for sufficiently large cross sections there is no advantage to several other more complex estimators.

Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (23)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:jfinqa:v:29:y:1994:i:02:p:279-300_00

Access Statistics for this article

More articles in Journal of Financial and Quantitative Analysis from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().