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Paper

Modeling Structural Changes: Bayesian Estimation of Multiple Changepoint Models and State Space Models Park, Jong Hee Uploaded 07-17-2006 Keywords Multiple changepoint model State space model Markov chain Monte Carlo methods Bayes factors Data augmentation. Abstract While theoretical models in political science are inspired by structural changes in politics, most empirical methods assume stable patterns of causal relationships. Static models with constant parameters do not properly capture dynamic changes in the data and lead to incorrect parameter estimates. In this paper, I introduce two Bayesian time series models, which can detect and estimate possible structural changes in temporal data: multiple changepoint models and state space models. To emphasize the utility of the models to political scientists, I show some examples in the context of discrete dependent variables. Then, I apply these models to an important debate in international politics over U.S. use of force abroad. The findings of the multiple changepoint and state space models demonstrate that the predictors of presidential use of force have shifted dramatically.

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Paper

Joint Modeling of Dynamic and Cross-Sectional Heterogeneity: Introducing Hidden Markov Panel Models Park, Jong Hee Uploaded 07-14-2009 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.