A Dynamic State-Space Model of Coded Political Texts

Martin Elff

Political Analysis, 2013, vol. 21, issue 2, 217-232

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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:21:y:2013:i:02:p:217-232_01

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