Asymptotic Optimality of Generalized CL, Cross-Validation, and Generalized Cross-Validation in Regression with Heteroskedastic Errors

The problem considered here is that of using a data-driven procedure to select a good estimate from a class of linear estimates indexed by a discrete parameter. In contrast to other papers on this subject, we consider models with heteroskedastic errors. The results apply to model selection problems in linear regression and to nonparametric regression estimation via series estimators, nearest neighbor estimators, and local regression estimators, among others. Generalized CL, cross-validation, and generalized cross-validation procedures are analyzed.

JEL Classification: 211

Keywords: Heteroskedasticity, linear regression, nonparametric regression, model selection, asymptotic theory, cross validation