Nonparametric estimation of regression functions with discrete regressors created by Desheng Ouyang, Qi Li and Jeffrey S. Racine

By:

Ouyang, Desheng [author]

Contributor(s):

Li, Qi [co author]

Material type: Text

TextSeries: Econometric theory ; Volume 25, number 1Cambridge: Cambridge University Press, 2009Content type:

text

Media type:

unmediated

Carrier type:

volume

ISSN:

02664666

Subject(s):

Regression analysis -- Nonparametric statistics -- Estimation theory

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Abstract: We consider the problem of estimating a nonparametric regression model containing categorical regressors only. We investigate the theoretical properties of least squares cross-validated smoothing parameter selection, establish the rate of convergence (to zero) of the smoothing parameters for relevant regressors, and show that there is a high probability that the smoothing parameters for irrelevant regressors converge to their upper bound values, thereby automatically smoothing out the irrelevant regressors. A small-scale simulation study shows that the proposed cross-validation-based estimator performs well in finite-sample settings.

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Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library - Special Collections	HB139.T52 ECO (Browse shelf(Opens below))	Vol. 25, no.1 (pages 1-42)	SP3256	Not for loan	For In House Use Only

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We consider the problem of estimating a nonparametric regression model containing categorical regressors only. We investigate the theoretical properties of least squares cross-validated smoothing parameter selection, establish the rate of convergence (to zero) of the smoothing parameters for relevant regressors, and show that there is a high probability that the smoothing parameters for irrelevant regressors converge to their upper bound values, thereby automatically smoothing out the irrelevant regressors. A small-scale simulation study shows that the proposed cross-validation-based estimator performs well in finite-sample settings.

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