Specification testing for errors-in-variables models

Taisuke Otsu and Luke Taylor

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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.

JEL-codes: J1 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2020-06-19
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Published in Econometric Theory, 19, June, 2020. ISSN: 0266-4666

Downloads: (external link)
http://eprints.lse.ac.uk/102690/ Open access version. (application/pdf)

Related works:
Journal Article: SPECIFICATION TESTING FOR ERRORS-IN-VARIABLES MODELS (2021)
Working Paper: Specification testing for errors-in-variables models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:102690

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