The Bias of the RSR Estimator and the Accuracy of Some Alternatives
分析了重复销售回归中截面异方差的影响，发现估计量存在偏差，并提出了无偏的最大似然替代方法MLRSR，模拟表明其更稳健准确。
This paper analyzes the implications of cross‐sectional heteroskedasticity in the 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 the bias in the average return estimate for each 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 an unbiased maximum likelihood alternative to the RSR that directly estimates index returns, which we term MLRSR. The unbiased MLRSR estimators are analogous to the RSR estimators but are arithmetic averages of individual asset returns. Simulations show that these estimators are robust to time‐varying cross‐sectional variance and that the MLRSR may be more accurate than RSR and some alternative methods.