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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
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

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.

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

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References

Breusch, T. S., Robertson, J. C., and Welsh, A. H. 1997. “The Emperor's New Clothes: A Critique of the Multivariate t Regression Model.” Statistica Neerlandica 51(3): 269286.CrossRefGoogle Scholar
Cox, Gary W. 1997. Making Votes Count: Strategic Coordination in the World's Electoral Systems. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Florczyka, Andrzej (ed). 1993. Wybory Do Sejmu Rzeczpospolitej Polskiej. Warsaw: Panstowa Komisja Wyborcza.Google Scholar
Gibson, John, and Cielecka, Anna. 1995. “Economic Influences on the Political Support for Market Reforms in Post-Communist Transitions: Some Evidence from the 1993 Polish Parliamentary Elections.” Europe-Asia Studies 47(5): 765785.CrossRefGoogle Scholar
Greene, William H. 2000. Econometric Analysis, 4th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Herron, Michael C. 1999. “Postestimation Uncertainty in Limited Dependent Variable Models.” Political Analysis 8:8398.CrossRefGoogle Scholar
Honaker, James, Katz, Jonathan N., and King, Gary. 2002. “A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data.” Political Analysis 10:84100.CrossRefGoogle Scholar
Jackman, Simon. 2000. “Estimation and Inference are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation.” Political Analysis 8:307332.CrossRefGoogle Scholar
Jackson, John E. 2002. “A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections.” Political Analysis 10:4965.CrossRefGoogle Scholar
Jackson, John E., Klich, Jacek, and Poznánska, Krystyna. 1998. “Democratic Institutions and Economic Reform: The Polish Case,” Working paper, Ann Arbor: University of Michigan.Google Scholar
Johnson, Richard A., and Wichern, Dean W. 1998. Applied Multivariate Statistical Analysis, 4th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Katz, Jonathan, and King, Gary. 1999. “A Statistical Model for Multiparty Electoral Data.” American Political Science Review 93(1): 1532.CrossRefGoogle Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. 2000. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347361.CrossRefGoogle Scholar
King, Gary, Honaker, Anne Joseph, James, and Scheve, Kenneth. 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review 95(1): 4969.CrossRefGoogle Scholar
Lange, Kenneth L., Little, Roderick J. A., and Taylor, Jeremy M. G. 1989. “Robust Statistical Modeling Using the t Distribution.” Journal of the American Statistical Association 84(408): 881896.Google Scholar
Mikhailov, Nikolai, Niemi, Richard G., and Weimer, David W. 2002. “Application of Theil Group Logit Methods to District-Level Vote Shares: Tests of Prospective and Retrospective Voting in the 1991, 1993, and 1997 Polish Elections.” Electoral Studies (in press).CrossRefGoogle Scholar
Theil, Henri. 1970. “On the Estimation of Relationships Involving Qualitative Variables.” American Journal of Sociology 76(1): 103154.CrossRefGoogle Scholar
Tomz, Michael, Wittenberg, Jason, and King, Gary. 2001. CLARIFY: Software for Interpreting and Presenting Statistical Results, Version 2.0. Cambridge, MA: Harvard University, June 1 (http://gking.harvard.edu/).Google Scholar
Tong, Y. L. 1990. The Multivariate Normal Distribution. New York: Springer-Verlag.CrossRefGoogle Scholar
Zellner, Arnold. 1962. “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias.” Journal of the American Statistical Association 57(298): 348368.CrossRefGoogle Scholar
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