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A Dynamic State-Space Model of Coded Political Texts

  • Martin Elff (a1)
Abstract

This article presents a new method of reconstructing actors' political positions from coded political texts. It is based on a model that combines a dynamic perspective on actors' political positions with a probabilistic account of how these positions are translated into emphases of policy topics in political texts. In the article it is shown how model parameters can be estimated based on a maximum marginal likelihood principle and how political actors' positions can be reconstructed using empirical Bayes techniques. For this purpose, a Monte Carlo Expectation Maximization algorithm is used that employs independent sample techniques with automatic Monte Carlo sample size adjustment. An example application is given by estimating a model of an economic policy space and a noneconomic policy space based on the data from the Comparative Manifesto Project. Parties' positions in policy spaces reconstructed using these models are made publicly available for download.

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Corresponding author
e-mail: martin.elff@uni-konstanz.de (corresponding author)
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Author's note: I wish to thank Ken Benoit, Ian Budge, Daniel Stegmüller, Shawn Treier, Paul Whiteley, participants at the 2008 Summer Political Methodology meeting and the 2012 MPSA General Conference, the Departmental Seminars of the Department of Government and the Department of Mathematical Sciences of the University of Essex, and of the Research Seminar at Nuffield College, University of Oxford, as well as two anonymous reviewers for helpful comments and suggestions on previous versions of the article. Replication material, software, and supplementary data are available online at the Political Analysis dataverse. An online appendix on further details is available on the Political Analysis Web site.

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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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