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Estimating class‐specific parametric models under class uncertainty: local polynomial regression clustering in an hedonic analysis of wine markets
Author(s) -
Costanigro Marco,
Mittelhammer Ron C.,
McCluskey Jill J.
Publication year - 2009
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1094
Subject(s) - cluster analysis , class (philosophy) , polynomial regression , mathematics , econometrics , nonparametric statistics , regression analysis , regression , parametric statistics , hedonic regression , nonparametric regression , semiparametric regression , local regression , polynomial , statistics , wine , computer science , artificial intelligence , mathematical analysis , physics , optics
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.