A framework for power analysis using a structural equation modelling procedure

BACKGROUND: This paper demonstrates how structural equation modelling (SEM) can be used as a tool to aid in carrying out power analyses. For many complex multivariate designs that are increasingly being employed, power analyses can be difficult to carry out, because the software available lacks sufficient flexibility. Satorra and Saris developed a method for estimating the power of the likelihood ratio test for structural equation models. Whilst the Satorra and Saris approach is familiar to researchers who use the structural equation modelling approach, it is less well known amongst other researchers. The SEM approach can be equivalent to other multivariate statistical tests, and therefore the Satorra and Saris approach to power analysis can be used. METHODS: The covariance matrix, along with a vector of means, relating to the alternative hypothesis is generated. This represents the hypothesised population effects. A model (representing the null hypothesis) is then tested in a structural equation model, using the population parameters as input. An analysis based on the chi-square of this model can provide estimates of the sample size required for different levels of power to reject the null hypothesis. CONCLUSIONS: The SEM based power analysis approach may prove useful for researchers designing research in the health and medical spheres

Human Capital Estimation through Structural Equation Models with some Categorical Observed Variables

The aim of this paper is to estimate, for US, Canada and Italy, the latent variable human capital and its causal relationship with labor income, through some Structural Equation Models. The analyzed models contain some observed categorical variables, which imply the use of the two-stage estimation technique.Human Capital ; Structural Equation Model (SEM) ; Polychoric Correlation ; Weighted Least Squares ; LISREL

On Bayesian structural inference in a simultaneous equation model

Econometric issues that are considered fundamental in the development of Bayesian structuralinference within a Simultaneous Equation Model are surveyed. The difficulty of specifying prior information which is of interest to economists and which yields tractable posterior and predictive distributions has started this line of research. A major issue is the nonstandard shape of the likelihood due to reduced rank restrictions. It implies that existence of structural posterior moments under vague prior information is a nontrivial issue. The problem is illustrated through simple examples using artificially generated data in a so-called limited information framework where the connection with the problem of weak instruments in classical econometrics is also described. A positive development is Bayesian inference of implied characteristics, in particular, dynamic features of a Simultaneous Equation Model. The potential of Bayesian structural inference, using a predictive approach for prior specification and using Monte Carlo simulation techniques for computational purposes, is illustrated by means of a prior and posterior analysis of the US business cycle in the period of the depression. A structural prior is elicited through investigation of the implied predictive features.

Medium-Size Macroeconomic Model for the Brazilian Economy

This paper presents a medium-scale structural model for the Brazilian economy with more than 30 equations. The potential output is derived from a Cobb-Douglas production function and the demand side is divided in estimated equation for: consumption of the families, investment in machinery and construction, government spending and net exports. The estimated Phillips curve has two interesting features: dummies for the structural break in the pass-through and also a term that includes labor productivity on the Phillips equation. An algorithm to run the model with a model consistent forward-looking term in the Phillips curve is implemented. There are long-run equilibrium conditions for the external and fiscal debts and also for the real interest rate. External and supply shocks hit the medium-size model to generate impulse responses in order to compare with small-scale structural models.