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Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors

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  • Peter Hall

    (Department of Mathematics and Statistics, University of Melbourne)

  • Qi Li

    (Department of Economics, Texas A&M University)

  • Jeffrey S. Racine

    (Department of Economics, McMaster University)

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.

Suggested Citation

  • Peter Hall & Qi Li & Jeffrey S. Racine, 2007. "Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 784-789, November.
  • Handle: RePEc:tpr:restat:v:89:y:2007:i:4:p:784-789
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