Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors

Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors

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. Simula...

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Veröffentlicht in:The review of economics and statistics 2007-11, Vol.89 (4), p.784-789
Hauptverfasser: Hall, Peter, Li, Qi, Racine, Jeffrey S.
Format: Artikel
Sprache:eng
Schlagworte:
Economic theory
Kernel functions
Markovs inequality
Nonparametric models
Parametric models
Regression analysis
Signal bandwidth
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Zusammenfassung: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.
ISSN:0034-6535
1530-9142
DOI:10.1162/rest.89.4.784