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Published August 7, 2017 | Submitted
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An Improved Statistical Model for Multiparty Electoral Data


Katz and King (1999) develop 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 politics 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 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.

Additional Information

An earlier version of the paper was presented at the annual meetings of the American Political Science Association, Washington, D.C., 2000 under the title "A Practical Statistical Model for Multiparty Electoral Data". For research support, we gratefully acknowledge the John M. Olin Foundation, the National Science Foundation (SBR-9729884, SBR-9753126, and IIS-9874747), the National Institutes of Aging, and the World Health Organization.

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