On Finite Sample Properties of Alternative Estimators of Coefficients in a Structural Equation with Many Instruments

T. W. Anderson, Naoto Kunitomo and Yukitoshi Matsushita
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T. W. Anderson: Department of Statistics and Department of Economics, Stanford University
Naoto Kunitomo: Faculty of Economics, University of Tokyo

No CIRJE-F-577, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: We compare four different estimation methods for the coefficients of a linear structural equation with instrumental variables. As the classical methods we consider the limited information maximum likelihood (LIML) estimator and the two-stage least squares (TSLS) estimator, and as the semi-parametric estimation methods we consider the maximum empirical likelihood (MEL) estimator and the generalized method of moments (GMM) (or the estimating equation) estimator. Tables and figures of the distribution functions of four estimators are given for enough values of the parameters to cover most linear models of interest and we include some heteroscedastic cases and nonlinear cases. We have found that the LIML estimator has good performance in terms of the bounded loss functions and probabilities when the number of instruments is large, that is, the micro-econometric models with "many instruments" in the terminology of recent econometric literature.

Pages: 34 pages
Date: 2008-07
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (14)

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