A Caltech Library Service

An Improved Statistical Model for Multiparty Electoral Data

Honaker, James and Katz, Jonathan N. and King, Gary (2001) An Improved Statistical Model for Multiparty Electoral Data. Social Science Working Paper, 1111. California Institute of Technology , Pasadena, CA. (Unpublished)

[img] PDF (sswp 1111 - Feb. 2001) - Submitted Version
See Usage Policy.


Use this Persistent URL to link to this item:


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.

Item Type:Report or Paper (Working Paper)
Katz, Jonathan N.0000-0002-5287-3503
King, Gary0000-0002-5327-7631
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.
Group:Social Science Working Papers
Funding AgencyGrant Number
John M. Olin FoundationUNSPECIFIED
National Institute on AgingUNSPECIFIED
World Health OrganizationUNSPECIFIED
Series Name:Social Science Working Paper
Issue or Number:1111
Record Number:CaltechAUTHORS:20170807-153549706
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79871
Deposited By: Jacquelyn Bussone
Deposited On:07 Aug 2017 23:14
Last Modified:09 Mar 2020 13:18

Repository Staff Only: item control page