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Proposed separability restriction tests using nonparametric regression methods

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  • Takaaki Aoki

Abstract

This article proposes some tests for separability restriction incorporating nonparametric regression methods, as well as offering their general statistic characteristics. An effective separability restriction test is essential for appropriate model specification or appropriate implementation of semi-parametric estimation. In this article, I describe two procedures to yield the estimated residuals, which is very sensitive to separability restriction, upon which one test statistics is proposed. In some benchmark models of sine/cosine functions, I simulate out the probability density function of test statistics in a small sample. These presented results and analysis show that the proposed estimator is robust and effective to variable functional form of regression curves and to variable scale factors, broader than the 'optimal' level, and can be put conveniently and widely into a practical use.

Suggested Citation

  • Takaaki Aoki, 2008. "Proposed separability restriction tests using nonparametric regression methods," Applied Economics Letters, Taylor & Francis Journals, vol. 15(12), pages 949-954.
    • Handle: RePEc:taf:apeclt:v:15:y:2008:i:12:p:949-954
    DOI: 10.1080/13504850600949160
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    References listed on IDEAS

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    1. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    2. James Heckman & Edward Vytlacil, 2001. "Identifying The Role Of Cognitive Ability In Explaining The Level Of And Change In The Return To Schooling," The Review of Economics and Statistics, MIT Press, vol. 83(1), pages 1-12, February.
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