NONPARAMETRIC ESTIMATION OF REGRESSION FUNCTIONS WITH DISCRETE REGRESSORS
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.
Year of publication: |
2009
|
---|---|
Authors: | Ouyang, Desheng ; Li, Qi ; Racine, Jeffrey S. |
Published in: |
Econometric Theory. - Cambridge University Press. - Vol. 25.2009, 01, p. 1-42
|
Publisher: |
Cambridge University Press |
Description of contents: | Abstract [journals.cambridge.org] |
Saved in:
Saved in favorites
Similar items by person
-
Categorial semiparametric varying-coefficient models
Li, Qi, (2013)
-
Nonparametric estimation of regression functions with discrete regressors
Ouyang, Desheng, (2009)
-
Categorical semiparametric varyingâcoefficient models
Li, QI, (2013)
- More ...