Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models

2001/11/01 by Whitney K. Newey · 4 citations
Economics, Econometrics and Finance · Mathematics · #Monetary Policy and Economic Impact #Statistical Methods and Inference

paper · doi:10.1162/003465301753237704

openalex publication_date 2001/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/04

Abstract

Nonlinear regression with measurement error is important for estimation from microeconomic data. One approach to identification and estimation is a causal model, in which the unobserved true variable is predicted by observable variables. This paper details the estimation of such a model using simulated moments and a flexible disturbance distribution. An estimator of the asymptotic variance is given for parametric models. Also, a semiparametric consistency result is given. The value of the estimator is demonstrated in a Monte Carlo study and an application to estimating Engel Curves.

Citations

• Consistent Nonparametric Regression
• Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models

Cited by

• Estimation of Nonlinear Models with Measurement Error
• Instrumental Variable Treatment of Nonclassical Measurement Error Models
• Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution
• Errors-in-variables model [wikipedia]