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Political Analysis Advance Access originally published online on September 10, 2007
Political Analysis 2008 16(2):179-196; doi:10.1093/pan/mpm023
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© The Author 2007. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Testing the Predictions of the Multidimensional Spatial Voting Model with Roll Call Data

Gyung-Ho Jeong

Department of Political Science, Washington University in St. Louis, Campus Box 1063, One Brookings Drive, St. Louis, MO 63130

e-mail: gjeong{at}artsci.wustl.edu

This paper develops a procedure for locating proposals and legislators in a multidimensional policy space by applying agenda-constrained ideal point estimation. Placing proposals and legislators on the same scale allows an empirical test of the predictions of the spatial voting model. I illustrate this procedure by testing the predictive power of the uncovered set—a solution concept of the multidimensional spatial voting model—using roll call data from the U.S. Senate. Since empirical tests of the predictive power of the uncovered set have been limited to experimental data, this is the first empirical test of the concept's predictive power using real-world data.

Author's note: An earlier version of this paper was presented at the 2006 Annual Meeting of Political Methodology Society. I am grateful to Andrew Martin, Gary Miller, Dan O'Neill, David Park, Robert Walker, and three anonymous reviewers for their helpful comments. I am especially indebted to Gary Miller for his insights and advice. All remaining errors are my own.


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