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On the Asymptotic Optimality of Alternative Minimum-Distance Estimators in Linear Latent-Variable Models

Published online by Cambridge University Press:  11 February 2009

Albert Satorra  and
Heinz Neudecker
Albert Satorra
Affiliation:
Universitat Pompeu Fabra, Barcelona
Heinz Neudecker
Affiliation:
Universiteit van Amsterdam
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Abstract

In the context of linear latent-variable models, and a general type of distribution of the data, the asymptotic optimality of a subvector of minimum-distance estimators whose weight matrix uses only second-order moments is investigated. The asymptotic optimality extends to the whole vector of parameter estimators, if additional restrictions on the third-order moments of the variables are imposed. Results related to the optimality of normal (pseudo) maximum likelihood methods are also encompassed. The results derived concern a wide class of latent-variable models and estimation methods used routinely in software for the analysis of latent-variable models such as LISREL, EQS, and CALIS. The general results are specialized to the context of multivariate regression and simultaneous equations with errors in variables.

Type
Articles
Information
Econometric Theory , Volume 10 , Issue 5 , December 1994 , pp. 867 - 883
Copyright
Copyright © Cambridge University Press 1994

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