Title

Testing additive separability of error term in nonparametric structural models

Publication Type

Journal Article

Publication Date

5-2015

Abstract

This article considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model nonadditively. We propose a test statistic under a set of identification conditions considered by Hoderlein et al. (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

Nonparametric structural equation, Nonseparable models, Hypotheses testing, Additive separability, C12, C13, C14

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometric Reviews

Volume

34

Issue

6-10

First Page

1056

Last Page

1087

ISSN

0747-4938

Identifier

10.1080/07474938.2014.956621

Publisher

Taylor & Francis: STM, Behavioural Science and Public Health Titles

Additional URL

http://dx.doi.org/10.1080/07474938.2014.956621

This document is currently not available here.

Share

COinS