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Simulated Asymptotic Least Squares Theory

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Author Info
Ramdan Dridi
Abstract

We develop in this paper a general econometric methodology referred to as the Simulated Asymptotic Least Squares (SALS). It is shown that this approach provides a unifying theory for 'approximation-based' or simulation-based inference methods and nests the Simulated Nonlinear Least Squares (SSNLS), the Simulated Pseudo Maximum Likelihood (SPML), the Simulated Method of Moments (SMM) in both parametric and semiparametric settings, the Indirect Inference (II) and the Efficient Method of Moments (EMM). We produce a new notion of Efficiency Bounds in Direction and provide a general study of the efficiency in the SALS framework. In the particular case of the II and the EMM methods and when the instrumental model is of a GMM type, we characterise a new weighting matrix for a more efficient estimation about the structural parameters of interest ?0. This new weighting matrix does no longer correspond, in the general case, to the classical one as characterised by Hansen (1982). Generalized global specification tests extending the previous existing ones are also proposed.

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Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2000/396.

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Date of creation: Jun 2000
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Handle: RePEc:cep:stiecm:/2000/396

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Keywords: Simulated Asymptotic Least Squares Approximation-based and Simulation-based estimation Efficiency Bounds in Direction GMM SNLS SPML SMM II GII EMM.

References listed on IDEAS
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  7. Laroque, Guy & Salanie, Bernard, 1989. "Estimation of Multi-market Fix-Price Models: An Application of Pseudo Maximum Likelihood Methods," Econometrica, Econometric Society, vol. 57(4), pages 831-60, July. [Downloadable!] (restricted)
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  1. Ramdan Dridi & Eric Renault, 2000. "Semi-Parametric Indirect Inference," STICERD - Econometrics Paper Series /2000/392, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
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