Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415381 | Computational Statistics & Data Analysis | 2008 | 21 Pages |
Abstract
A regression model whose regression function is the sum of a linear and a nonparametric component is presented. The design is random and the response and explanatory variables satisfy mixing conditions. A new local polynomial type estimator for the nonparametric component of the model is proposed and its asymptotic normality is obtained. Specifically, this estimator works on a prewhitening transformation of the dependent variable, and the results show that it is asymptotically more efficient than the conventional estimator (which works on the original dependent variable) when the errors of the model are autocorrelated. A simulation study and an application to a real data set give promising results.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
G. Aneiros-Pérez, J.M. Vilar-Fernández,