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Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999

Published online by Cambridge University Press:  04 January 2017

Andrew D. Martin  and
Kevin M. Quinn
Andrew D. Martin
Affiliation:
Department of Political Science, Washington University, Campus Box 1063, One Brookings Drive, St. Louis, MO 63130-4899. e-mail: [email protected]
Kevin M. Quinn
Affiliation:
Department of Political Science and Center for Statistics in the Social Sciences, Box 354320, University of Washington, Seattle, WA 98195-4322. e-mail: [email protected]
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Abstract

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At the heart of attitudinal and strategic explanations of judicial behavior is the assumption that justices have policy preferences. In this paper we employ Markov chain Monte Carlo methods to fit a Bayesian measurement model of ideal points for all justices serving on the U.S. Supreme Court from 1953 through 1999. We are particularly interested in determining to what extent ideal points of justices change throughout their tenure on the Court. This is important because judicial politics scholars oftentimes invoke preference measures that are time invariant. To investigate preference change, we posit a dynamic item response model that allows ideal points to change systematically over time. Additionally, we introduce Bayesian methods for fitting multivariate dynamic linear models to political scientists. Our results suggest that many justices do not have temporally constant ideal points. Moreover, our ideal point estimates outperform existing measures and explain judicial behavior quite well across civil rights, civil liberties, economics, and federalism cases.

Type
Articles
Information
Political Analysis , Volume 10 , Issue 2 , Spring 2002 , pp. 134 - 153
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2002 

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