The Bias of the RSR Estimator and the Accuracy of Some Alternatives / William N. Goetzmann, Liang Peng.

Format:

Book

Author/Creator:

Goetzmann, William N.

Contributor:

National Bureau of Economic Research.

Peng, Liang.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0270.

NBER technical working paper series no. t0270

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2001.

Summary:

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.

Notes:

Print version record

April 2001.