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A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data

Published online by Cambridge University Press:  04 January 2017

James Honaker
Affiliation:
Department of Political Science, University of California, Los Angeles, Los Angeles, CA 90095-1472. e-mail: tercer@ucla.edu
Jonathan N. Katz
Affiliation:
Division of Humanities and Social Science, California Institute of Technology, Pasadena, CA 91125. e-mail: jkatz@caltech.edu
Gary King
Affiliation:
Department of Government, Harvard University, Cambridge, MA 02138. e-mail: king@harvard.edu

Abstract

Katz and King have previously developed a model for predicting or explaining aggregate electoral results in multiparty democracies. Their model is, in principle, analogous to what least-squares regression provides American political researchers in that two-party system. Katz and King applied their model to three-party elections in England and revealed a variety of new features of incumbency advantage and sources of party support. Although the mathematics of their statistical model covers any number of political parties, it is computationally demanding, and hence slow and numerically imprecise, with more than three parties. In this paper we produce an approximate method that works in practice with many parties without making too many theoretical compromises. Our approach is to treat the problem as one of missing data. This allows us to use a modification of the fast EMis algorithm of King, Honaker, Joseph, and Scheve and to provide easy-to-use software, while retaining the attractive features of the Katz and King model, such as the t distribution and explicit models for uncontested seats.

Type
Research Article
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2002 

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