The Bias of the RSR Estimator and the Accuracy of Some Alternatives
William Goetzmann () and
No 270, NBER Technical Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: R2 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Published as Goetzmann, W. N. and L. Peng. "The Bias Of The RSR Estimator And The Accuracy Of Some Alternatives," Real Estate Economics, 2002, v30(1,Spring), 13-39.
Downloads: (external link)
Journal Article: The Bias of the RSR Estimator and the Accuracy of Some Alternatives (2002)
Working Paper: The Bias of the RSR Estimator and the Accuracy of Some Alternatives (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberte:0270
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in NBER Technical Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().