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

Author

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  • William Goetzmann
  • Liang Peng
Abstract
This paper analyzes the implications of cross-sectional hetero- skedasticity 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

  • William Goetzmann & Liang Peng, 2001. "The Bias of the RSR Estimator and the Accuracy of Some Alternatives," Yale School of Management Working Papers ysm174, Yale School of Management, revised 01 Mar 2001.
    • Handle: RePEc:ysm:wpaper:ysm174
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    References listed on IDEAS

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