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Post-stratification without population level information on the post-stratifying variable, with application to political polling Gelman, Andrew Katz, Jonathan Riley, Cavan Uploaded 02-10-2000 Keywords Bayesian Inference Post-stratification Sample surveys State-space models Abstract We investigate the construction of more precise estimates of a collection of population means using information about a related variable in the context of repeated sample surveys. The method is illustrated using poll results concerning presidential approval rating (our related variable is political party identification). We use post-stratification to construct these improved estimates, but since we don't have population level information on the post-stratifying variable, we construct a model for the manner in which the post-stratifier develops over time. In this manner, we obtain more precise estimates without making possibly untenable assumptions about the dynamics of our variable of interest, the presidential approval rating.

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Modeling Time Series Count Data: A State-Space Approach to Event Counts Brandt, Patrick T. Williams, John T. Fordham, Benjamin Uploaded 07-08-1998 Keywords Poisson models event counts state-space models Kalman filter non-normal time series Abstract This is a revised version, dated July 16, 1998. Time series count data is prevalent in political science. We argue that political scientists should employ time series methods to analyze time series count data. A simple state-space model is presented that extends the Kalman filter to count data. The properties of this model are outlined and further evaluated by a Monte Carlo study. We then show how time series of counts present special problems by turning to two replications: the number of hospital deaths that are the subject of a recent criminal court case, and Pollins (1996) MIDs data from international relations.

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Bayesian Analysis of Structural Changes: Historical Changes in US Presidential Uses of Force Abroad Park, Jong Hee Uploaded 07-16-2007 Keywords structural changes changepoint models discrete time series data use of force data state space models time-varying parameter models Bayesian inference Abstract While many theoretical models in political science are inspired by structural changes in politics, most empirical methods assume stable patterns of causal processes and fail to capture dynamic changes in theoretical relationships. In this paper, I introduce an efficient Bayesian approach to the multiple changepoint problem presented by Chib (1998) and discuss the utility of the Bayesian changepoint models in the context of generalized linear models. As an illustration, I revisit the debate over whether and how U.S. presidents have used forces abroad in response to domestic factors since 1890.