Title:      Joint Modeling of Dynamic and Cross-Sectional
Heterogeneity: Introducing Hidden Markov Panel Models

Authors:    Jong Hee Park

Entrydate:  2009-07-14 19:59:45

Keywords:   Bayesian statistics, Fixed-effects, Hidden Markov
models, Markov chain Monte Carlo methods, Random-effects,
Reversible jump Markov chain Monte Carlo

Abstract:   Researchers working with panel data sets often face
situations where changes in unobserved factors have produced
changes in the cross-sectional heterogeneity across time
periods. Unfortunately, conventional statistical methods for
panel data are based on the assumption that the unobserved
cross-sectional heterogeneity is time constant. In this paper, I
introduce statistical methods to diagnose and model changes in
the unobserved heterogeneity. First, I develop three
combinations of a hidden Markov model with panel data models
using the Bayesian framework; (1) a baseline hidden Markov panel
model with varying fixed effects and varying random effects; (2)
a hidden Markov panel model with varying fixed effects; and (3)
a hidden Markov panel model with varying intercepts. Second, I
present model selection methods to diagnose the dynamic
heterogeneity using the marginal likelihood method and the
reversible jump Markov chain Monte Carlo method. I illustrate
the utility of these methods using two important ongoing
political economy debates; the relationship between income
inequality and economic growth and the effect of institutions on
income inequality.

http://polmeth.wustl.edu/retrieve.php?id=916

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