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Models for Spatially Dependent Missing Data
Authors:James P LeSage  R Kelley Pace
Institution:(1) Department of Economics, University of Toledo, Toledo, OH, 43606, U.S.A;(2) LREC Endowed Chair of Real Estate, Department of Finance, E.J. Ourso College of Business Administration, Louisiana State University, Baton Rouge, LA, 70803-6308, U.S.A
Abstract:Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known characteristics. In fact, explanatory variable information on house characteristics for all properties, sold and unsold, are often available from assessors. We set forth an estimation approach that predicts missing values of the dependent variable when the sample data exhibit spatial dependence. Employing information on the housing characteristics of both sold and unsold properties can improve prediction, increase estimation efficiency for the missing-at-random case, and reduce self-selection bias in the non-missing-at-random case. We demonstrate these advantages with a Monte Carlo experiment as well as with actual housing data.
Keywords:spatial missing data  EM algorithm  sparse matrices  assessment  spatial sample selectivity  hedonic pricing
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