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A Dynamic State-Space Model of Coded Political Texts

Published online by Cambridge University Press:  04 January 2017

Martin Elff*
Affiliation:
Fachbereich Politik- und Verwaltungswissenschaft, Universität Konstanz, Universitätsstraße 10, D-78464, Konstanz, Germany
*
e-mail: martin.elff@uni-konstanz.de (corresponding author)
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Abstract

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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.

Information

Type
Research Article
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 
Supplementary material: PDF

Elff supplementary material

Appendix

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