Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable

TLDR: In this paper, the authors consider a model in which one observes multiple indicators and multiple causes of a single latent variable and derive the maximum-likelihood estimators and their asymptotic variance-covariance matrix.

Abstract: We consider a model in which one observes multiple indicators and multiple causes of a single latent variable. In terms of the multivariate regression of the indicators on the causes, the model implies restrictions of two types: (i) the regression coefficient matrix has rank one, (ii) the residual variance-covariance matrix satisfies a factor analysis model with one common factor. The first type of restriction is familiar to econometricians and the second to psychometricians. We derive the maximum-likelihood estimators and their asymptotic variance-covariance matrix. Two alternative “limited information” estimators are also considered and compared with the maximum-likelihood estimators in terms of efficiency.