NONPARAMETRIC ESTIMATION OF REGRESSION FUNCTIONS WITH DISCRETE REGRESSORS Academic Article

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.

authors

• Li, Qi

published proceedings

• ECONOMETRIC THEORY

author list (cited authors)

• Ouyang, D., Li, Q. i., & Racine, J. S.

citation count

• 21

complete list of authors

• Ouyang, Desheng||Li, Qi||Racine, Jeffrey S

publication date

• February 2009

publisher

• Cambridge University Press (CUP)  Publisher

published in

• ECONOMETRIC THEORY  Journal

keywords

• 38 Economics
• 3802 Econometrics
• 49 Mathematical Sciences
• 4905 Statistics

Digital Object Identifier (DOI)

• 10.1017/S0266466608090014

start page

• 1

end page

• 42

volume

• 25

issue

• 1

URL

• http://dx.doi.org/10.1017/s0266466608090014