Improving Hedonic Estimation with an Inequality Restricted Estimator

Economists commonly estimate the value of characteristics not traded in explicit markets by hedonic pricing. Unfortunately, these non-explicitly traded characteristics often display a lack of independent variation or multicollinearity. Often some prior information on the value of these characteristi...

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Bibliographic Details
Published inThe review of economics and statistics Vol. 77; no. 4; pp. 609 - 621
Main Authors Gilley, Otis W., Pace, R. Kelley
Format Journal Article
LanguageEnglish
Published Cambridge, Mass Elsevier Science Publishers 01.11.1995
Harvard University Press, etc
MIT Press Journals, The
Subjects
A priori knowledge
Automobiles
Bayesian analysis
Bedrooms
Collinearity
Economic models
Economic theory
Economics
Error rates
Estimating techniques
Estimation methods
Estimators
Housing
Institutional review boards
Monte Carlo methods
Monte Carlo simulation
Prices
Pricing policies
Studies
Symposium on Hedonic Methods in Industrial Economics
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Summary:Economists commonly estimate the value of characteristics not traded in explicit markets by hedonic pricing. Unfortunately, these non-explicitly traded characteristics often display a lack of independent variation or multicollinearity. Often some prior information on the value of these characteristics is available from submarkets. This paper utilizes this type of prior information to circumvent multicollinearity problems in hedonic pricing models using an inequality restricted Bayesian (IRB) estimator. We perform a Monte Carlo experiment and cross-validation analysis to demonstrate the superiority of IRB over OLS at many margins in a variety of situations typically faced in hedonic estimation.
ISSN:0034-6535
1530-9142
DOI:10.2307/2109810