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Fast Estimation of Ideal Points with Massive Data

Published online by Cambridge University Press:  28 December 2016

KOSUKE IMAI ,
JAMES LO  and
JONATHAN OLMSTED
KOSUKE IMAI*
Affiliation:
Princeton University
JAMES LO*
Affiliation:
University of Southern California
JONATHAN OLMSTED*
Affiliation:
The NPD Group
*
Kosuke Imai is Professor, Department of Politics and Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08544. Phone: 609-258-6601 (kimai@princeton.edu), URL: http://imai.princeton.edu.
James Lo is Assistant Professor, Department of Political Science, University of Southern California, Los Angeles, CA 90089 (lojames@usc.edu).
Jonathan Olmsted is Solutions Manager, NPD Group, Port Washington, NY 11050 (jpolmsted@gmail.com).
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Abstract

Estimation of ideological positions among voters, legislators, and other actors is central to many subfields of political science. Recent applications include large data sets of various types including roll calls, surveys, and textual and social media data. To overcome the resulting computational challenges, we propose fast estimation methods for ideal points with massive data. We derive the expectation-maximization (EM) algorithms to estimate the standard ideal point model with binary, ordinal, and continuous outcome variables. We then extend this methodology to dynamic and hierarchical ideal point models by developing variational EM algorithms for approximate inference. We demonstrate the computational efficiency and scalability of our methodology through a variety of real and simulated data. In cases where a standard Markov chain Monte Carlo algorithm would require several days to compute ideal points, the proposed algorithm can produce essentially identical estimates within minutes. Open-source software is available for implementing the proposed methods.

Type
Research Article
Information
American Political Science Review , Volume 110 , Issue 4 , November 2016 , pp. 631 - 656
Copyright
Copyright © American Political Science Association 2016 

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