Publication Type
Journal Article
Version
submittedVersion
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
Citation
SU, Liangjun; TU, Yundong; and ULLAH, Aman.
Testing Additive Separability of Error Term in Nonparametric Structural Models. (2015). Econometric Reviews. 34, (6-10), 1057-1088.
Available at: https://ink.library.smu.edu.sg/soe_research/1432
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07474938.2014.956621