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:title: A Dynamic State-Space Model of Coded Political Texts

.. bibsource:: ../publications.bib
.. pub-details:: elff:dynamic.model.coded.texts
        
.. 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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   - `Replication material </files/Elff-DynamicModel-ReplicationMaterial.zip>`__
   - :ref:`Data on parties' political positions based on the method <data-parties-positions>`
   -  A :ref:`software package <manifestos-package>` that implements the method in *R*


.. dropdown:: Open Access at Cambridge University Press
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   - `Full article <https://doi.org/10.1093/pan/mps042>`__
   - `Online appendix <https://static.cambridge.org/content/id/urn:cambridge.org:id:article:S104719870001336X/resource/name/S104719870001336Xsup001.pdf>`__
   - `Replication dataverse <https://doi.org/10.7910/DVN/2ATYFU>`__