Asymptotic Optimality of Generalized CL, Cross-Validation, and Generalized Cross-Validation in Regression with Heteroskedastic ErrorsAuthor(s):
Publication Date: May 1989
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.
Heteroskedasticity, linear regression, nonparametric regression, model selection, asymptotic theory, cross validation
JEL Classification Codes: 211
See CFP: 790