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

Citation:

James Honaker, Gary King, and Jonathan N Katz. 2002. “A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data.” Political Analysis, 10: 84–100, Winter. Copy at http://j.mp/lEBSVa
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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.