Analysis of Multivariate Binary Random Effect Models using Hierarchical Likelihood Approach

This paper proposes using hierarchical-likelihood estimation method for binary panel data models featuring state dependence and unmeasured heterogeneity. Hierarchical generalized linear models are an extension of generalized linear models in that they combine generalized linear models with random effects. Hierarchical likelihood based upon hierarchical generalized linear models provides a useful tool for analyzing multivariate data with correlation. These models allow various regression models for mean parameters of response variable as well as dispersion parameters. For a inference, the hierarchical- likelihood approach is suggested for a useful too as model selection and residual plots with real data analysis. In this paper, we suggest hierarchical-likelihood approach estimators for binary random effects-models, and show that this approach outperforms existing marginal likelihood estimators at low computation cost by simulation studies. And then, we add its usefulness by analyzing a real example.