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Title:A bayesian approach to model-based clustering for panel probit models PDF Logo
Authors:Aßmann, Christian
Boysen-Hogrefe, Jens
Issue Date:2009
Series/Report no.:Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2009,03
Abstract:Consideration 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.
Subjects:Bayesian Estimation
MCMC Methods
Panel Probit Model
Mixture Modelling
JEL:C11
C23
C25
Document Type:Working Paper
Appears in Collections:Economics Working Papers, Department of Economics, CAU Kiel

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