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

Political Analysis, Vol. 10, No. 1, pp. 84-100, Winter 2002

17 Pages Posted: 13 Jan 2008

See all articles by James Honaker

James Honaker

University of California, Los Angeles (UCLA) - Department of Political Science

Jonathan N. Katz

California Institute of Technology - Division of the Humanities and Social Sciences

Gary King

Harvard University

Abstract

Katz and King (1999) develop a model for predicting or explaining aggregate electoral results in multiparty democracies. This model is, in principle, analogous to what least squares regression provides American politics researchers in that two-party system. Katz and King applied this model to three-party elections in England and revealed a variety of new features of incumbency advantage and where each party pulls support from. Although the mathematics of their statistical model covers any number of political parties, it is computationally very demanding, and hence slow and numerically imprecise, with more than three. The original goal of our work was to produce an approximate method that works quicker in practice with many parties without making too many theoretical compromises. As it turns out, the method we offer here improves on Katz and King's (in bias, variance, numerical stability, and computational speed) even when the latter is computationally feasible. We also offer easy-to-use software that implements our suggestions.

Suggested Citation

Honaker, James and Katz, Jonathan N. and King, Gary, A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data. Political Analysis, Vol. 10, No. 1, pp. 84-100, Winter 2002, Available at SSRN: https://ssrn.com/abstract=1082901

James Honaker (Contact Author)

University of California, Los Angeles (UCLA) - Department of Political Science ( email )

405 Hilgard Ave.
Los Angeles, CA 90095-1472
United States

Jonathan N. Katz

California Institute of Technology - Division of the Humanities and Social Sciences ( email )

1200 East California Blvd.
Pasadena, CA 91125
United States
626-395-4191 (Phone)

HOME PAGE: http://jkatz.caltech.edu

Gary King

Harvard University ( email )

1737 Cambridge St.
Institute for Quantitative Social Science
Cambridge, MA 02138
United States
617-500-7570 (Phone)

HOME PAGE: http://gking.harvard.edu

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