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Association: Name: The Midwest Political Science Association URL: http://www.indiana.edu/~mpsa/
Citation: URL: http://www.allacademic.com/meta/p83123_index.html Direct Link: HTML Code:

Granberg-Rademacker, Scott. "IS BAYESIAN ANALYSIS SUPERIOR? ABENCHMARKED COMPARISON OF REGRESSION AND BAYESIAN ANALYSIS ONINCOMPLETE STATE-LEVEL DATA" Paper presented at the annual meeting of the The Midwest Political Science Association, Palmer House Hilton, Chicago, Illinois, Apr 15, 2004 <Not Available>. 2009-05-26 <http://www.allacademic.com/meta/p83123_index.html>

Granberg-Rademacker, S. , 2004-04-15 "IS BAYESIAN ANALYSIS SUPERIOR? ABENCHMARKED COMPARISON OF REGRESSION AND BAYESIAN ANALYSIS ONINCOMPLETE STATE-LEVEL DATA" Paper presented at the annual meeting of the The Midwest Political Science Association, Palmer House Hilton, Chicago, Illinois Online <.PDF>. 2009-05-26 from http://www.allacademic.com/meta/p83123_index.html

Publication Type: Conference Paper/Unpublished Manuscript Review Method: Peer Reviewed Abstract: The question addressed in this paper is whether or not Monte Carlo Markov Chain (MCMC) Bayesian analysis does a superior job of assessing probable parameters when compared to standard Ordinary Least Squares (OLS) given state-level data with a significant amount of missing data. Because a good deal of state-level data is collected by states themselves, incomplete or missing data is a common problem, as some states do a better job of reporting certain types of data. Yet many researchers continue to use OLS despite Bayesian claims that MCMC could provide superior parameter estimation under such conditions. This paper uses data from the State Politics and Policy Quarterly (SPPQ) and Monte Carlo methods to test that assertion under conditions where state-level data has a substantial number of missing cases.

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