TY - JOUR
AU - Goetzmann,William N.
AU - Peng,Liang
TI - The Bias of the RSR Estimator and the Accuracy of Some Alternatives
JF - National Bureau of Economic Research Technical Working Paper Series
VL - No. 270
PY - 2001
Y2 - April 2001
DO - 10.3386/t0270
UR - http://www.nber.org/papers/t0270
L1 - http://www.nber.org/papers/t0270.pdf
N1 - Author contact info:
William N. Goetzmann
School of Management
Yale University
Box 208200
New Haven, CT 06520-8200
Tel: 203/432-5950
Fax: 203/432-3003
E-Mail: william.goetzmann@yale.edu
Liang Peng
University of Colorado at Boulder
419 UCB
Boulder, CO 80309-419
E-Mail: liang.peng@colorado.edu
AB - 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.
ER -