The Accuracy Of Real Estimate Indices: Repeat Sale Estimators
Simulation techniques allow the author to examine the behavior and accuracy of several repeat sales regression estimators used to construct real estate return indices. He shows that the generalized least squares (GLS) method is the maximum likelihood estimator, and he shows how estimation accuracy can be significantly improved through a Bayesian approach. In addition, he introduces a biased estimation procedure based upon the James and Stein method to address the problems of multicollinearity common to the procedure. Copyright 1992 by Kluwer Academic Publishers
(This abstract was borrowed from another version of this item.)
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||1990|
|Date of revision:|
|Contact details of provider:|| Postal: U.S.A.; COLUMBIA UNIVERSITY, GRADUATE SCHOOL OF BUSINESS, PAINE WEBBER , New York, NY 10027 U.S.A|
Phone: (212) 854-5553
Web page: http://www.gsb.columbia.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:fth:colubu:fb-_90-17. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Krichel)
If references are entirely missing, you can add them using this form.