Department of Economics and Business Economics

Luke Nicholas Taylor

Specification Testing for Errors-in-Variables Models

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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.

Original languageEnglish
JournalEconometric Theory
Volume37
Issue4
Pages (from-to)747-768
Number of pages22
ISSN0266-4666
DOIs
Publication statusPublished - Aug 2021

    Research areas

  • CHECKING, DECONVOLUTION, INTEGRATED SQUARE ERROR, PARAMETER, REGRESSION-MODEL

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