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The Bias of the Rsr Estimator and the Accuracy of Some Alternatives

36 Pages Posted: 29 Mar 2001  

Liang Peng

Smeal College of Business, The Pennsylvania State University

William N. Goetzmann

Yale School of Management - International Center for Finance; National Bureau of Economic Research (NBER)

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Date Written: April 2001

Abstract

This paper analyzes the implications of cross-sectional heteroskedasticity in repeat sales regression (RSR). RSR estimators are essentially geometric averages of individual asset returns because of the logarithmic transformation of price relatives. We show that the cross sectional variance of asset returns affects the magnitude of bias in the average return estimate for that period, while reducing the bias for the surrounding periods. It is not easy to use an approximation method to correct the bias problem. We suggest a maximum-likelihood alternative to the RSR that directly estimates index returns that are analogous to the RSR estimators but are arithmetic averages of individual returns. Simulations show that these estimators are robust to time-varying cross-sectional variance and may be more accurate than RSR and some alternative methods of RSR.

Suggested Citation

Peng , Liang and Goetzmann, William N., The Bias of the Rsr Estimator and the Accuracy of Some Alternatives (April 2001). NBER Working Paper No. t0270. Available at SSRN: https://ssrn.com/abstract=265279

Liang Peng

Smeal College of Business, The Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

William N. Goetzmann (Contact Author)

Yale School of Management - International Center for Finance ( email )

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New Haven, CT 06520-8200
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203-436-9252 (Fax)

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