 Estimation of Structural Nonlinear Errors-in-Variables Models by Simulated Least-Squares MethodCheng Hsiao and Q Kevin Wang International Economic Review, 2000, vol. 41, issue 2, 523-42 Abstract: 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. Date: 2000 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ier:iecrev:v:41:y:2000:i:2:p:523-42 Ordering information: This journal article can be ordered from Access Statistics for this article International Economic Review is currently edited by Harold L. Cole More articles in International Economic Review from Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104-6297. Contact information at EDIRC. |
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