Asymptotic Optimality of Generalized C_{L}, Cross-Validation, and Generalized Cross-Validation in Regression with Heteroskedastic Errors

Donald Andrews ()

No 906, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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 C_{L}, cross-validation, and generalized cross-validation procedures are analyzed.

Keywords: Heteroskedasticity; linear regression; nonparametric regression; model selection; asymptotic theory; cross validation (search for similar items in EconPapers)
Pages: 24 pages
Date: 1989-05
Note: CFP 790.
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Published in Journal of Econometrics (1991), 47: 359-377

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Persistent link: https://EconPapers.repec.org/RePEc:cwl:cwldpp:906

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