:orphan:
: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.

- `Data`_ on parties' political positions based on the method rep
- `A software package`_ that implements the method in *R*

.. _Data: /research/data
.. _`A software package`: /software/manifestos