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Identification with latent variables

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  • L.L. Wegge

Abstract

This is an essay on a unified approach to the identifiability problem in static models with and without hidden endogenous variables. As is well known, when some of these variables are unobserved, the prior information requirements for models when all endogenous variables are observed, are still there. In addition, extra prior information that takes the place of the means and covariances of the missing variables will have to be supplied directly or indirectly by the statistical researcher. In the paper we characterize the quality and quantity of the required information for the general linear static model and apply it when the model is i) an econometric demand and supply model with missing observations on the quantity transacted, ii) a factor analysis model with observed characteristics of the test takers and iii) a LISREL Model without fixed exogenous variables. With unknown true parameters, the exact rank conditions are seldom verifiable but we do recommend an implementable check‐list that is adequate for almost all parameters.

Suggested Citation

  • L.L. Wegge, 1991. "Identification with latent variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 45(2), pages 121-143, June.
    • Handle: RePEc:bla:stanee:v:45:y:1991:i:2:p:121-143
    DOI: 10.1111/j.1467-9574.1991.tb01299.x
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    Cited by:

    1. Wegge, Leon L.F., 1981. "ARMAX-Model Parameter Identification without and with Latent Variables," Working Papers 225920, University of California, Davis, Department of Economics.
    2. Wegge, Leon L., 1996. "Local identifiability of the factor analysis and measurement error model parameter," Journal of Econometrics, Elsevier, vol. 70(2), pages 351-382, February.
    3. MOUCHART, Michel & SAN MARTIN , Ernesto, 1998. "Identification problems in a class of mixture models with an application to the LISREL model," LIDAM Discussion Papers CORE 1998025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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