Proposed separability restriction tests using nonparametric regression methods

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.