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Checking the Adequacy for A Distortion Errors-In-Variables Parametric Regression Model

id: 2269 Date: 20160218 Times:
Magazines   83 (2015) 52–64
AuthorJun Zhang, Gaorong Li, Zhenghui Feng
ContentThis paper studies tools for checking the validity of a parametric regression model, when both response and predictors are unobserved and distorted in a multiplicative fashion by an observed confounding variable. A residual based empirical process test statistic marked by proper functions of the regressors is proposed. We derive asymptotic distribution of the proposed empirical process test statistic: a centered Gaussian process under the null hypothesis and a non-centered one under local alternatives converging to the null hypothesis at parametric rates. We also suggest a bootstrap procedure to calculate critical values. Simulation studies are conducted to demonstrate the performance of the proposed test statistic and real examples are analyzed for illustrations.
JEL-Codes
KeywordsConfounding variables Errors-in-variables Distorting functions Empirical process
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