Estimation of Structural Nonlinear Errors-in-Variables Models by Simulated Least-Squares Method.

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. Copyright 2000 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.