Estimating class-specific parametric models under class uncertainty: local polynomial regression clustering in an hedonic analysis of wine markets

Marco Costanigro, Ron C. Mittelhammer and Jill J. McCluskey
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Marco Costanigro: Colorado State University, Fort Collins, CO, USA, Postal: Colorado State University, Fort Collins, CO, USA
Ron C. Mittelhammer: School of Economic Sciences and Department of Statistics, Washington State University, Pullman, WA, USA, Postal: School of Economic Sciences and Department of Statistics, Washington State University, Pullman, WA, USA
Jill J. McCluskey: School of Economic Sciences, Washington State University, Pullman, WA, USA, Postal: School of Economic Sciences, Washington State University, Pullman, WA, USA

Journal of Applied Econometrics, 2009, vol. 24, issue 7, pages 1117-1135

Abstract: We introduce a method for estimating multiple class regression models when class membership is uncertain. The procedure-local polynomial regression clustering-first estimates a nonparametric model via local polynomial regression, and then identifies the underlying classes by aggregating sample observations into data clusters with similar estimates of the (local) functional relationships between dependent and independent variables. Finally, parametric functions specific to each class are estimated. The technique is applied to the estimation of a multiple-class hedonic model for wine, resulting in the identification of four distinct wine classes based on differences in implicit prices of the attributes. Copyright © 2009 John Wiley & Sons, Ltd.

Date: 2009

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