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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 

References

Albert, James H. 1992. “Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling.” Journal of Educational Statistics 17:251–269.Google Scholar
Albert, James H., and Chib, Siddhartha. 1993. “Bayesian Analysis of Binary and Polychotomous Response Data.” Journal of the American Statistical Association 88:669–679.Google Scholar
Bailey, Michael, and Chang, Kelly H. 2001. “Comparing Presidents, Senators, and Justices: Interinstitutional Preference Estimation.” Journal of Law, Economics, & Organization 17:477–506.CrossRefGoogle Scholar
Baum, Lawrence. 1988. “Measure Policy Change in the United States Supreme Court.” American Political Science Review 82:905–912.CrossRefGoogle Scholar
Beck, Nathaniel. 1989. “Estimating Dynamic Models Using Kalman Filtering.” Political Analysis 1:121–156.CrossRefGoogle Scholar
Beck, Nathaniel, and Katz, Jonathan N. 1995. “What to Do (and Not to Do) with Time-Series Cross-Section Data.” American Political Science Review 89:634–647.CrossRefGoogle Scholar
Carter, C. K., and Kohn, R. 1994. “One Gibbs Sampling for State-Space Models.” Biometrika 81:541–533.Google Scholar
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2000. “The Statistical Analysis of Legislative Behavior: A Unified Approach.” Paper presented at the Annual Meeting of the Political Methodology Society.Google Scholar
Enelow, James, and Hinich, Melvin. 1984. The Spatial Theory of Voting: An Introduction. Cambridge: Cambridge University Press.Google Scholar
Epstein, Lee, Hoekstra, Valerie, Segal, Jeffrey A., and Spaeth, Harold J. 1998. “Do Political Preferences Change: A Longitudinal Study of U.S. Supreme Court Justices.” Journal of Politics 60:801–818.CrossRefGoogle Scholar
Epstein, Lee, and Knight, Jack. 1998. The Choices Justices Make. Washington, DC: CQ Press.Google Scholar
Epstein, Lee, and Mershon, Carol. 1996. “Measuring Political Preferences.” American Journal of Political Science 40:261–294.CrossRefGoogle Scholar
Epstein, Lee, Walker, Thomas G., and Dixon, William J. 1989. “The Supreme Court and Criminal Justice Disputes: A Neo-Institutional Perspective.” American Journal of Political Science 33:825–841.Google Scholar
Epstein, Lee, Segal, Jeffrey A., Spaeth, Harold J., and Walker, Thomas G. 2001. The Supreme Court Compendium: Data, Decisions, and Developments, 2nd ed. Washington, DC: CQ Press.Google Scholar
Eskridge, William N. 1991. “Overriding Supreme Court Statutory Interpretation Decisions.” Yale Law Journal 101:331–417.CrossRefGoogle Scholar
Frühwirth-Schnatter, Sylvia. 1994. “Data Augmentation and Dynamic Linear Models.” Journal of Time Series Analysis 15:183–202.CrossRefGoogle Scholar
Jackman, Simon. 2001. “Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking.” Political Analysis 9:227–241.CrossRefGoogle Scholar
Johnson, Valen E., and Albert, James H. 1999. Ordinal Data Modeling. New York: Springer.CrossRefGoogle Scholar
Londregan, John. 2000. “Estimating Legislators' Preferred Points.” Political Analysis 8:35–56.Google Scholar
Martin, Andrew D., and Quinn, Kevin M. 2001a. “Estimating Latent Structures of Voting for Micro-Committees, with Application to the U.S. Supreme Court.” Paper presented at the Annual Meeting of the Midwest Political Science Association.Google Scholar
Martin, Andrew D., and Quinn, Kevin M. 2001b. “Scythe Statistical Library, Release 0.1.” http://scythe.wustl.edu/.Google Scholar
Patz, Richard J., and Junker, Brian W. 1999. “A Straightforward Approach to Markov Chain Monte Carlo Methods for Item Response Models.” Journal of Educational and Behavioral Statistics 24:146–178.CrossRefGoogle Scholar
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A Political-Economic History of Roll-Call Voting. Oxford: Oxford University Press.Google Scholar
Rohde, David W., and Spaeth, Harold J. 1976. Supreme Court Decision Making. San Francisco: W. H. Freeman.Google Scholar
Schubert, Glendon. 1965. The Judicial Mind: The Attitudes and Ideologies of Supreme Court Justices, 1946-1963. Evanston, IL: Northwestern University Press.Google Scholar
Schubert, Glendon. 1974. The Judicial Mind Revisited: Psychometric Analysis of Supreme Court Ideology. London: Oxford University Press.Google Scholar
Segal, Jeffrey A., and Cover, Albert D. 1989. “Ideological Values and the Votes of U.S. Supreme Court Justices.” American Political Science Review 83:557–565.CrossRefGoogle Scholar
Segal, Jeffrey A., and Spaeth, Harold J. 1993. The Supreme Court and the Attitudinal Model. Cambridge: Cambridge University Press.Google Scholar
Spaeth, Harold J. 2001. United States Supreme Court Judicial Database, 1953-2000 Terms [Computer File], 16th ed. Ann Arbor, MI: Inter-University Consortium for Political and Social Research.Google Scholar
Stimson, James A. 1985. “Regression in Space and Time: A Statistical Essay.” American Journal of Political Science 29:914–947.CrossRefGoogle Scholar
Tate, C. Neal, and Handberg, Roger. 1991. “Time Binding and Theory Building in Personal Attribute Models of Supreme Court Voting Behavior, 1916-88.” American Journal of Political Science 35:460–480.Google Scholar
West, Mike, and Harrison, Jeff. 1997. Bayesian Forecasting and Dynamic Models. New York: Springer.Google Scholar