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An Easy and Accurate Regression Model for Multiparty Electoral Data

Published online by Cambridge University Press:  04 January 2017

Michael Tomz,
Joshua A. Tucker and
Jason Wittenberg
Michael Tomz
Affiliation:
Department of Political Science, Stanford University, Stanford, CA 94305-6044. e-mail: tomz@stanford.edu
Joshua A. Tucker
Affiliation:
Department of Politics and Woodrow Wilson School, Princeton University, Princeton, NJ 08544. e-mail: jtucker@princeton.edu
Jason Wittenberg
Affiliation:
Department of Political Science, University of Wisconsin, Madison, Madison, WI 53706-1389. e-mail: witty@polisci.wisc.edu
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Abstract

Katz and King have previously proposed a statistical model for multiparty election data. They argue that ordinary least-squares (OLS) regression is inappropriate when the dependent variable measures the share of the vote going to each party, and they recommend a superior technique. Regrettably, the Katz-King model requires a high level of statistical expertise and is computationally demanding for more than three political parties. We offer a sophisticated yet convenient alternative that involves seemingly unrelated regression (SUR). SUR is nearly as easy to use as OLS yet performs as well as the Katz-King model in predicting the distribution of votes and the composition of parliament. Moreover, it scales easily to an arbitrarily large number of parties. The model has been incorporated into Clarify, a statistical suite that is available free on the Internet.

Information

Type
Research Article
Information
Political Analysis , Volume 10 , Issue 1 , Winter 2002, pp. 66 - 83
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2002 

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