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Estimation of Structural Nonlinear Errors‐in‐Varibles Models by Simulated Least‐Squares Method[Note 1. This work was completed while the first author was ...]
Author(s) -
Hsiao Cheng,
Wang Q. Kevin
Publication year - 2000
Publication title -
international economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.658
H-Index - 86
eISSN - 1468-2354
pISSN - 0020-6598
DOI - 10.1111/1468-2354.00074
Subject(s) - estimator , non linear least squares , least squares function approximation , mathematics , covariance matrix , covariance , nonlinear system , simple (philosophy) , covariate , generalized least squares , total least squares , mathematical optimization , algorithm , computer science , statistics , singular value decomposition , philosophy , physics , epistemology , quantum mechanics
This article proposes a simulation approach to obtain least‐squares or generalized least‐squares estimators of structural nonlinear errors‐in‐variables models. The proposed estimators are computationally attractive because they do not need numerical integration nor huge numbers of simulations per observable. In addition, the asymptotic covariance matrix of the estimator has a simple decomposition that may be used to guide selection of appropriate simulation sizes. The method is also useful for models with missing data or imperfect surrogate covariates, where application of conventional least‐squares and maximum‐likelihood methods is restricted by numerical multidimensional integrations.