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.
