Publication Type

Journal Article

Publication Date

5-2017

Abstract

In this paper we propose a novel consistent model specification test based on the martingale difference divergence (MDD) of the error term given the covariates. The MDD equals zero if and only if error term is conditionally mean independent of the covariates. Our MDD test does not require any nonparametric estimation under the alternative and it is applicable even if we have many covariates in the regression model. We establish the asymptotic distributions of our test statistic under the null and a sequence of Pitman local alternatives converging to the null at the usual parametric rate. Simulations suggest that our MDD test has superb performance in terms of both size and power and it generally dominates several competitors. In particular, it’s the only test that has well controlled size in the presence of many covariates and reasonable power against high frequency alternatives as well.

Keywords

Distance covariance, Integrated conditional moment test, Martingale difference divergence, Martingale difference correlation, Specification test

Discipline

Behavioral Economics | Econometrics

Research Areas

Econometrics

Publication

Economics Letters

Volume

156

First Page

162

Last Page

167

ISSN

0165-1765

Identifier

10.1016/j.econlet.2017.05.002

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1016/j.econlet.2017.05.002

Share

COinS