Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors

Peter Hall, Qi Li and Jeffrey Racine
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Peter Hall: Department of Mathematics and Statistics, University of Melbourne

The Review of Economics and Statistics, 2007, vol. 89, issue 4, 784-789

Abstract: In this paper we consider a nonparametric regression model that admits a mix of continuous and discrete regressors, some of which may in fact be redundant (that is, irrelevant). We show that, asymptotically, a data-driven least squares cross-validation method can remove irrelevant regressors. Simulations reveal that this "automatic dimensionality reduction" feature is very effective in finite-sample settings. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Date: 2007
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