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Agenda Constrained Legislator Ideal Points and the Spatial Voting Model

Published online by Cambridge University Press:  04 January 2017

Joshua D. Clinton  and
Adam Meirowitz
Joshua D. Clinton
Affiliation:
Department of Political Science, Stanford University, Stanford, CA 94305. e-mail: jclinton@stanford.edu
Adam Meirowitz
Affiliation:
Graduate School of Business, Stanford University, Stanford, CA 94305. e-mail: ameirow@stanford.edu Department of Politics, Princeton University, Princeton, NJ 08544
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Abstract

Existing preference estimation procedures do not incorporate the full structure of the spatial model of voting, as they fail to use the sequential nature of the agenda. In the maximum likelihood framework, the consequences of this omission may be far-reaching. First, information useful for the identification of the model is neglected. Specifically, information that identifies the proposal locations is ignored. Second, the dimensionality of the policy space may be incorrectly estimated. Third, preference and proposal location estimates are incorrect and difficult to interpret in terms of the spatial model. We also show that the Bayesian simulation approach to ideal point estimation (Clinton et al. 2000; Jackman 2000) may be improved through the use of information about the legislative agenda. This point is illustrated by comparing several preference estimators of the first U.S. House (1789–1791).

Type
Research Article
Information
Political Analysis , Volume 9 , Issue 3 , January 2001 , pp. 242 - 259
Copyright
Copyright © 2001 by the Society for Political Methodology 

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