Publication Type

Journal Article

Publication Date

5-2015

Abstract

This paper considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model non-additively. We propose a test statistic under a set of identification conditions considered by Hoderlein, Su and White (2012), which require the existence of a control variable such that the regressor is independent of the error term given the control variable. The test statistic is motivated from the observation that, under the additive error structure, the partial derivative of the nonparametric structural function with respect to the error term is one under identification. The asymptotic distribution of the test is established and a bootstrap version is proposed to enhance its finite sample performance. Monte Carlo simulations show that the test has proper size and reasonable power in finite samples.

Keywords

Additive Separability, Hypotheses Testing, Nonparametric Structural Equation, Nonseparable Models

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometric Reviews

Volume

34

Issue

6-10

First Page

1057

Last Page

1088

ISSN

0747- 4938

Identifier

10.1080/07474938.2014.956621

Publisher

Taylor and Francis

Copyright Owner and License

Authors

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/10.1080/07474938.2014.956621

Included in

Econometrics Commons

Share

COinS