title:         Modeling Structural Changes: Bayesian Estimation of Multiple
Changepoint Models and State Space Models
authors:       Jong Hee Park
entrydate:     2006-07-17 14:12:55
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.

http://polmeth.wustl.edu/retrieve.php?id=621

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