Estimation of nonlinear models with Berkson measurement errors

Nonlinear Models of Measurement Errors

Measure errors in economic data are pervasive and nontrivial in size. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. The importance of measurement errors in analyzing the empirical implications of economic theories is highlighted in Milton Friedman’s seminal book on the consumption...

Minimum Distance Regression Model Checking with Berkson Measurement Errors

Lack-of-fit testing of a regression model with Berkson measurement error has not been discussed in the literature to date. To fill this void, we propose a class of tests based on minimized integrated square distances between a nonparametric regression function estimator and the parametric model being fitted. We prove asymptotic normality of these test statistics under the null hypothesis and th...

Model Checking in Partial Linear Regression Models with Berkson Measurement Errors

This paper discusses the problem of fitting a parametric model to the nonparametric component in partially linear regression models when covariates in parametric and nonparametric parts are subject to Berkson measurement errors. The proposed test is based on the supremum of a martingale transform of a certain partial sum process of calibrated residuals. Asymptotic null distribution of this tran...

Nonlinear Models of Measurement Errors By

Longer Version of Identification and Estimation of Nonlinear Models Using Two Samples with Nonclassical Measurement Errors

This paper considers identification and estimation of a general nonlinear Errors-in-Variables (EIV) model using two samples. Both samples consist of a dependent variable, some error-free covariates, and an error-prone covariate, for which the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values; and neither sample contains an accurate measur...