Improving Hedonic Estimation with an Inequality Restricted Estimator

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Abstract
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.

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