Estimation of structural equation models with exact and stochastic prior information

Abstract

Recently, structural equation models are widely used in assessing data in economical and behavioral researches. To give more freedom in defining the structures of the model and obtain more precise and meaningful interpretations to the data, prior informations about the unknown parameters are usually incorporated in the analysis. In this article, basic estimation theory of structural equation models with both exact and stochastic prior informations is developed via the generalized least squares approach. Asymptotic properties of the estimator are derived and an iterative algorithm is implemented to obtain the estimates. An illustrative example is reported.

Keywords:

• Prior information
• Stochastic constraints
• Structural equation models
• Generalized least square
• Maximum likelihood
• Asymptotic properfies