Model checking in errors-in-variables regression

Weixing Song

Journal of Multivariate Analysis, 2008, vol. 99, issue 10, 2406-2443

Abstract: This paper discusses a class of minimum distance tests for fitting a parametric regression model to a class of regression functions in the errors-in-variables model. These tests are based on certain minimized distances between a nonparametric regression function estimator and a deconvolution kernel estimator of the conditional expectation of the parametric model being fitted. The paper establishes the asymptotic normality of the proposed test statistics under the null hypothesis and that of the corresponding minimum distance estimators. We also prove the consistency of the proposed tests against a fixed alternative and obtain the asymptotic distributions for general local alternatives. Simulation studies show that the testing procedures are quite satisfactory in the preservation of the finite sample level and in terms of a power comparison.

Keywords: primary; 62G08 secondary; 62G10 Errors-in-variables model Deconvolution kernel estimator Minimum distance Lack-of-fit test (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (7)

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