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Fast Estimation of Ideal Points with Massive Data

  • KOSUKE IMAI (a1), JAMES LO (a2) and JONATHAN OLMSTED (a3)

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.

Copyright

Corresponding author

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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American Political Science Review
  • ISSN: 0003-0554
  • EISSN: 1537-5943
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