A bayesian approach to model-based clustering for panel probit models
AbstractConsideration of latent heterogeneity is of special importance in non linear models for gauging correctly the effect of explaining variables on the dependent variable. This paper adopts the stratified model-based clustering approach for modeling latent heterogeneity for panel probit models. Within a Bayesian framework an estimation algorithm dealing with the inherent label switching problem is provided. Determination of the number of clusters is based on the marginal likelihood and out-of-sample criteria. The ability to decide on the correct number of clusters is assessed within a simulation study indicating high accuracy for both approaches. Different concepts of marginal effects incorporating latent heterogeneity at different degrees arise within the considered model setup and are directly at hand within Bayesian estimation via MCMC methodology. An empirical illustration of the developed methodology indicates that consideration of latent heterogeneity via latent clusters provides the preferred model specification compared to a pooled and a random coefficient specification. --
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Bibliographic InfoPaper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2009,03.
Date of creation: 2009
Date of revision:
Bayesian Estimation; MCMC Methods; Panel Probit Model; Mixture Modelling;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
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