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Latent variables, causal models and overidentifying constraints
 Journal of Econometrics
, 1988
"... When is a statistical dependency between two variables best explained by the supposition that one of these variables causes the other, as opposed to the supposition that there is a (possibly unmeasured) common cause acting on both variables? In this paper, we describe an approach towards model speci ..."
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When is a statistical dependency between two variables best explained by the supposition that one of these variables causes the other, as opposed to the supposition that there is a (possibly unmeasured) common cause acting on both variables? In this paper, we describe an approach towards model specification developed more fully in our book Discovering Cuud Structure, and illustrate its application to the aforementioned question. Briefly, the approach is to determine constraints satisfied by the variancecovariance matrix of a sample, and then to conduct a quasiautomated search for the causal specifications that will best explain those constraints, 1.
Journal of Econometrics 22 (1983) 4365. NorthHolland Publishing Company LATENT VARIABLE STRUCTURAL EQUATION MODELING WITH CATEGORICAL DATA*
"... Structural equation modeling with latent variables is overviewed for situations involving a mixture of dichotomous, ordered polytomous, and continuous indicators of latent variables. Special emphasis is placed on categorical variables, Models in psychometrics, econometrics and biometrics are interre ..."
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Structural equation modeling with latent variables is overviewed for situations involving a mixture of dichotomous, ordered polytomous, and continuous indicators of latent variables. Special emphasis is placed on categorical variables, Models in psychometrics, econometrics and biometrics are interrelated via a general model due to Muthen. Limited information least squares estimators and full information estimation are discussed. An example is estimated with a model for a fourwave longitudinal data set, where dichotomous responses are related to each other and a set of independent variables via latent variables with a variance component structure. 1.