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Small Chamber Ideal Point Estimation

Published online by Cambridge University Press:  04 January 2017

Michael Peress
Michael Peress*
Affiliation:
Department of Political Science, University of Rochester, 326 Harkness Hall, University of Rochester, Rochester, NY 14627
*
e-mail: mperess@mail.rochester.edu (corresponding author)
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Abstract

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Ideal point estimation is a topic of central importance in political science. Published work relying on the ideal point estimates of Poole and Rosenthal for the U.S. Congress is too numerous to list. Recent work has applied ideal point estimation to the state legislatures, Latin American chambers, the Supreme Court, and many other chambers. Although most existing ideal point estimators perform well when the number of voters and the number of bills is large, some important applications involve small chambers. We develop an estimator that does not suffer from the incidental parameters problem and, hence, can be used to estimate ideal points in small chambers. Our Monte Carlo experiments show that our estimator offers an improvement over conventional estimators for small chambers. We apply our estimator to estimate the ideal points of Supreme Court justices in a multidimensional space.

Information

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 3 , Summer 2009 , pp. 276 - 290
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: The author would like to thank Michael Bailey, Tasos Kalandrakis, Jeff Lewis, Keith Poole, the anonymous referees, and the participants of seminars at the American Political Science Association meetings (Philadelphia 2006) and the Midwest Political Science Association meetings (Chicago 2007) for their helpful comments and suggestions.

References

Andersen, Erling Bernhard. 1970. Asymptotic properties of conditional maximum-likelihood estimators. Journal of the Royal Statistical Society 32: 283–301.Google Scholar
Andersen, Erling Bernhard. 1973. Conditional inference and models for measuring. Copenhagen, Denmark: Mentalhygiejnisk Forsknings Institut.Google Scholar
Bafumi, James, Gelman, Andrew, Park, David K., and Kaplan, Noah. 2005. Practical issues in implementing and understanding Bayesian ideal point estimation. Political Analysis 13: 171–87.Google Scholar
Bailey, Michael A. 2001. Ideal point estimation with a small number of votes: A random effects approach. Political Analysis 9: 192–210.CrossRefGoogle Scholar
Bock, R. Darell, and Aitken, Murray. 1981. Marginal maximum likelihood estimation of item parameters: Application of the EM algorithm. Psychometrika 46: 443–59.Google Scholar
Carroll, Royce, Lewis, Jeffrey B., Lo, James, Poole, Keith T., and Rosenthal, Howard. Forthcoming. Comparing NOMINATE and IDEAL: Points of difference and Monte Carlo tests. Legislative Studies Quarterly.Google Scholar
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2003. The statistical analysis of roll call data. Working Paper.Google Scholar
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2004. The statistical analysis of roll call data. American Political Science Review 98: 355–70.Google Scholar
Desposato, Scott. 2004. The impact of party-switching on legislative behavior in Brazil. Working Paper.Google Scholar
Firth, David. 1993. Bias reduction of maximum likelihood estimates. Biometrika 80: 27–38.CrossRefGoogle Scholar
Geweke, John, Keane, Michael, and Runkle, David. 1994. Alternative computational approaches to inference in the multinomial probit model. Review of Economics and Statistics 76: 609–32.Google Scholar
Geweke, John, Keane, Michael, and Runkle, David. 1997. Statistical inference in the multiperiod multinomial probit model. Journal of Econometrics 81: 125–66.Google Scholar
Hall, Peter. 1997. The bootstrap and the Edgeworth expansion. New York: Springer.Google Scholar
Heckman, James J., and Snyder, James M. 1997. Linear probability models of the demand for attributes with an empirical application to estimating the preferences of legislators. RAND Journal of Economics 28: S142–89.Google Scholar
Hsiao, Cheng. 1986. Analysis of panel sata. Cambridge: Cambridge University Press.Google Scholar
Kiefer, J., and Wolfowitz, J. 1956. Consistency of the maximum likelihood estimator in the presence of infinitely many incidental parameters. Annals of Mathematical Statistics 27: 887–906.Google Scholar
Lancaster, Tony. 2000. The incidental parameter problem since 1948. Journal of Econometrics 95: 391–413.CrossRefGoogle Scholar
Lewis, Jeffrey B. 2001. Estimating voter preference distributions from individual level voting data. Political Analysis 9: 275–97.Google Scholar
Liittschwager, J. M., and Wang, C. 1978. Integer programming solution of a classification problem. Management Science 24: 1515–25.Google Scholar
Londregan, John. 2000a. Estimating legislator's preferred points. Political Analysis 8: 35–56.Google Scholar
Londregan, John. 2000b. Legislative institutions and ideology in Chile's democratic transition. Cambridge: Cambridge University Press.Google Scholar
Martin, Andrew D., and Quinn, Kevin M. 2002. Dynamic ideal point estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999. Political Analysis 10: 134–53.Google Scholar
Neyman, J., and Scott, Elizabeth L. 1948. Consistent Estimates Based on Partially Consistent Observations. Econometrica 16: 1–32.Google Scholar
Peress, Michael. 2007. Securing the base: Electoral competition under variable turnout. Working Paper.Google Scholar
Poole, Keith T. 2005. Spatial models of parliamentary voting. New York: Cambridge University Press.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A political economic history of roll call voting. New York: Oxford University Press.Google Scholar
Quinn, Kevin M., Park, Jong Hee, and Martin, Andrew D. 2007. Improving judicial ideal point estimates with a more realistic model of opinion content. Working Paper.Google Scholar
Spaeth, Harold J. 1999. United State Supreme Court judicial database, 1953–1998 terms [Computer File]. 15th ed. Ann Arbor, MI: Inter University Consortium of Political and Social Research.Google Scholar
Wright, Gerald C., and Osborne, Tracy. 2002. Party and roll call voting in the American legislature. Working Paper.Google Scholar
Zorn, Christopher. 2005. A solution to separation in binary response models. Political Analysis 13: 157–70.Google Scholar