A nonparametric test of fit of a parametric model

We propose a natural test of fit of a parametric regression model. The test is based on a comparison of a nonparametric kernel estimate of a regression function with its least-squares parametric estimate. Under the null hypothesis we derive approximations to the probability distribution functions of the test statistic. The approximations are exact with a power rate. Moreover, we prove the consistency of the test.

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Year of publication:	1991
Authors:	Kozek, Andrzej S.
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 37.1991, 1, p. 66-75
Publisher:	Elsevier
Keywords:	least squares method maximum deviation distribution nonlinear regression nonparametric regression parametric regression test of fit

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005106933