CFDP 906

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


Publication Date: May 1989

Pages: 24


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