Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors

Peter Hall; Qi Li; Jeffrey S. Racine

doi:10.1162/rest.89.4.784

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Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors

Peter Hall¹; Qi Li²; Jeffrey S. Racine³

¹ The University of Melbourne · ² Texas A&M University · ³ McMaster University

The Review of Economics and Statistics 2007

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.

DOI: 10.1162/rest.89.4.784
Volume: 89 (4)
Pages: 784-789
Language: en
Export: BibTeX
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