Estimation and Evaluation of Loan Discrimination: An Informational Approach
Marsha Courchane,
Amos Golan and
David Nickerson
Journal of Housing Research, 2000, vol. 11, issue 1, 67-90
Abstract:
Many recent studies have analyzed whether lending discrimination exists. In all previous studies, the researcher faces constraints with the available data or modeling problems. In this article, we use a new informational-based approach for evaluating loan discrimination. Given limited and noisy data, we develop a framework for estimating and evaluating discrimination in mortgage lending. This new informational-based approach performs well even when the data are limited or ill conditioned, or when the covariates are highly correlated. Because most data sets collected by bank examiners or banks suffer from some or all of these data problems, the more traditional estimation methods may fail to provide stable and efficient estimates.This new estimator can be viewed as a generalized maximum likelihood estimator.We provide inference and diagnostic properties of this estimator, presenting both sampling experiments and empirical analyses. For two of the three banks analyzed, we observe some evidence of potential racial discrimination.
Date: 2000
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/10835547.2000.12091955 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:rjrhxx:v:11:y:2000:i:1:p:67-90
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rjrh20
DOI: 10.1080/10835547.2000.12091955
Access Statistics for this article
Journal of Housing Research is currently edited by Kimberly Goodwin
More articles in Journal of Housing Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().