SPECIFICATION TESTING FOR ERRORS-IN-VARIABLES MODELS

Taisuke Otsu and Luke Taylor

Econometric Theory, 2021, vol. 37, issue 4, 747-768

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.

Date: 2021
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Related works:
Working Paper: Specification testing for errors-in-variables models (2020)
Working Paper: Specification testing for errors-in-variables models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:37:y:2021:i:4:p:747-768_4

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