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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.

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

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