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Below results based on the criteria 'Bayes factors'
Total number of records returned: 2
Operationalizing and Testing Spatial Theories of Voting
Quinn, Kevin M.
Martin, Andrew D.
Spatial models of voting behavior provide the foundation for a substantial number of theoretical results. Nonetheless, empirical work involving the spatial model faces a number of potential difficulties. First, measures of the latent voter and candidate issue positions must be obtained. Second, evaluating the fit of competing statistical models of voter choice is often more complicated than previously realized. In this paper, we discuss precisely these issues. We argue that confirmatory factor analysis applied to mass-level issue preference questions is an attractive means of measuring voter ideal points. We also show how party issue positions can be recovered using a variation of this strategy. We go on to discuss the problems of assessing the fit of competing statistical models (multinomial logit vs. multinomial probit) and competing explanations (those based on spatial theory vs. those derived from other theories of voting such as sociological theories). We demonstrate how the Bayesian perspective not only provides computational advantages in the case of fitting the multinomial probit model, but also how it facilitates both types of comparison mentioned above. Results from the Netherlands and Denmark suggest that even when the computational cost of multinomial probit is disregarded, the decision whether to use multinomial probit (MNP) or multinomial logit (MNL) is not clear-cut.
Modeling Structural Changes: Bayesian Estimation of Multiple Changepoint Models and State Space Models
Park, Jong Hee
Multiple changepoint model
State space model
Markov chain Monte Carlo methods
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.