Fast Estimation of Ideal Points with Massive Data

Kosuke Imai, James Lo and Jonathan Olmsted

American Political Science Review, 2016, vol. 110, issue 4, 631-656

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.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:apsrev:v:110:y:2016:i:04:p:631-656_00

Access Statistics for this article

More articles in American Political Science Review from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().