title: Presidential Approval: the case of George W. Bush
authors: Nathaniel Beck, Simon Jackman, Howard Rosenthal
entrydate: 2006-07-19 12:19:14
keywords: presidential approval, public opinion, polls, house effects, dynamic linear model, Bayesian statistics, Markov chain Monte Carlo, state space, pages of killer graphs
abstract: We use a Bayesian dynamic linear model to track approval for George W. Bush over time. Our analysis deals with several issues that have been usually addressed separately in the extant literature. First, our analysis uses polling data collected at a higher frequency than is typical, using over 1,100 published national polls, and data on macro-economic conditions collected at the weekly level. By combining this much poll information, we are much better poised to examine the public's reactions to events over shorter time scales than can the typical analysis of approval that utilizes monthly or quarterly approval. Second, our statistical modeling explicitly deals with the sampling error of these polls, as well as the possibility of bias in the polls due to house effects. Indeed, quite aside from the question of ``what drives approval?'', there is considerable interest in the extent to which polling organizations systematically diverge from one another in assessing approval for the president. These bias parameters are not only necessary parts of any realistic model of approval that utilizes data from multiple polling organizations, but easily estimated via the Bayesian dynamics linear model.
http://polmeth.wustl.edu/retrieve.php?id=627
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