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Specification testing for errors-in-variables models

Otsu, Taisuke and Taylor, Luke (2020) Specification testing for errors-in-variables models. Econometric Theory. ISSN 0266-4666

[img] Text (Specification testing for errors-in-variables models) - Accepted Version
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Identification Number: 10.1017/S0266466620000262


This paper considers specification testing for regression models with errors-in-variables and proposes a test statistic comparing the distance between the parametric and nonparametric fits based on deconvolution techniques. In contrast to the methods proposed by Hall and Ma (2007, Annals of Statistics, 35, 2620-2638) and Song (2008, Journal of Multivariate Analysis, 99, 2406-2443), our test allows general nonlinear regression models and possesses complementary local power properties. We establish the asymptotic properties of our test statistic for the ordinary and supersmooth measurement error densities. Simulation results endorse our theoretical findings: our test has advantages in detecting high-frequency alternatives and dominates the existing tests under certain specifications.

Item Type: Article
Official URL:
Additional Information: © 2020 Cambridge University Press
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
Date Deposited: 29 Nov 2019 10:21
Last Modified: 24 Nov 2023 06:00

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