TY - JOUR
T1 - Specification testing for errors-in-variables models
AU - Otsu, Taisuke
AU - Taylor, Luke
N1 - Funding Information:
*The authors would like to thank anonymous referees and a co-editor for helpful comments, and acknowledge financial support from the ERC Consolidator Grant (SNP 615882) (T.O.) and the ESRC (L.T.). Address correspondence to Taisuke Otsu, Department of Economics, London School of Economics, Houghton Street, London WC2A 2AE, UK; e-mail: t.otsu@lse.ac.uk.
Publisher Copyright:
©
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
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U2 - 10.1017/S0266466620000262
DO - 10.1017/S0266466620000262
M3 - Article
AN - SCOPUS:85087166380
VL - 37
SP - 747
EP - 768
JO - Econometric Theory
JF - Econometric Theory
SN - 0266-4666
IS - 4
ER -