On the Asymptotic Optimality of Alternative Minimum-Distance Estimators in Linear Latent-Variable Models
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.
Volume (Year): 10 (1994)
Issue (Month): 05 (December)
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References listed on IDEAS
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- Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
- Anderson, T. W., 1989. "Linear latent variable models and covariance structures," Journal of Econometrics, Elsevier, vol. 41(1), pages 91-119, May.
- Bentler, P. M., 1983. "Simultaneous equation systems as moment structure models : With an introduction to latent variable models," Journal of Econometrics, Elsevier, vol. 22(1-2), pages 13-42.
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