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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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Copyright
Corresponding author
e-mail: martin.elff@uni-konstanz.de (corresponding author)
Footnotes
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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.

Footnotes
References
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Adams, J., and Merrill, S. III. 2009. Policy-seeking parties in a parliamentary democracy with proportional representation: A valence-uncertainty model. British Journal of Political Science 39(3): 539–58.
Agresti, A. 2002. Categorical data analysis. 2nd ed. New York: Wiley.
Alesina, A. 1988. Credibility and policy convergence in a two-party system with rational voters. American Economic Review 78(4): 796805.
Benoit, K., and Laver, M. 2006. Party policy in modern democracies. London/New York: Routledge.
Benoit, K., Mikhaylov, S., and Laver, M. 2009. Treating words as data with error: Uncertainty in text statements of policy positions. American Journal of Political Science 53(2): 495513.
Booth, J. G., and Hobert, J. P. 1999. Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 61(1): 265–85.
Budge, I., and Farlie, D. 1983. Party competition: selective emphasis or direct confrontation? An alternative view with data. In Western European party systems continuity and change, eds. Daalder, H. and Mair, P., 265305. Beverly Hills, CA: Sage.
Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., and Tanenbaum, E. 2001. Mapping policy preferences: Estimates for parties, electors, and governments 1945–98. Oxford: Oxford University Press.
Budge, I., Robertson, D., and Hearl, D. 1987. Ideology, strategy, and party change: Spatial analysis of post war election programs in 19 democracies. Cambridge, MA: Cambridge University Press.
Caffo, B. S., Jank, W., and Jones, G. L. 2005. Ascent-based Monte Carlo expectation-maximization. Journal of the Royal Statistical Society, Series B (Methodological) 67(2): 235–51.
Clinton, J., Jackman, S., and Rivers, D. 2004. The statistical analysis of roll call data. American Political Science Review 98(2): 355–70.
Dempster, A. P., Laird, N. M., and Rubin, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological) 39(1): 138.
Downs, A. 1957. An economic theory of democracy. New York: Harper & Row.
Eddelbuettel, D., and François, R. 2011. Rcpp: Seamless R and C++ integration. Journal of Statistical Software 40(8): 118.
Elff, M. 2009. Social divisions, party positions, and electoral behaviour. Electoral Studies 28(2): 297308.
Elff, M. 2012. Replication data for: A dynamic state-space model of coded political texts. http://hdl.handle.net/1902.1/19198 (accessed December 11, 2012) IQSS Dataverse Network [Distributor] V1 [Version].
Enelow, J. M., and Hinich, M. 1984. The spatial theory of voting: An introduction. Cambridge, MA: Cambridge University Press.
Enelow, J. M., and Hinich, M. 1990. Advances in the spatial theory of voting. Cambridge, MA: Cambridge University Press.
Evans, G., Heath, A., and Payne, C. 1999. Class: Labour as a catch-all party? In Critical elections: British parties and voters in long-term perspective, eds. Evans, G. and Norris, P. London; Thousand Oakes, CA; New Delhi: Sage Publications.
Ezrow, L. 2007. The variance matters: How party systems represent the preferences of voters. Journal of Politics 69(1): 182–92.
Fielding, S. 2003. The Labour party: Continuity and change in the making of “New” Labour. New York: Palgrave Macmillan.
Francois, R., Eddelbuettel, D., and Bates, D. 2011. RcppArmadillo: Rcpp integration for Armadillo templated linear algebra library. R package version 0.2.18. http://cran.r-project.org/web/packages/RcppArmadillo/. (accessed December 23, 2012).
Gentle, J. E. 2003. Random number generation and Monte Carlo methods. 2nd ed. New York: Springer.
Heath, A., Jowell, R., Curtice, J., Evans, G., Field, J., and Witherspoon, S. 1991. Understanding political change: The British voter 1964–1987. Oxford: Pergamon Press.
Hinich, M. J., and Munger, M. C. 1994. Ideology and the theory of political choice. Ann Arbor: University of Michigan Press.
Klingemann, H.-D., Volkens, A., Bara, J., Budge, I., and McDonald, M. 2006. Mapping policy preferences II. Estimates for parties, electors, and governments in Eastern Europe, the European Union, and the OECD, 1990–2003. Oxford: Oxford University Press.
Kollman, K., Miller, J. H., and Page, S. E. 1992a. Adaptive parties in spatial elections. American Political Science Review 86(4): 929–37.
Kollman, K., Miller, J. H., and Page, S. E. 1992b. Political parties and electoral landscapes. British Journal of Political Science 28(1): 139–58.
Laver, M. 2001. Position and salience in the policies of political actors. In Estimating the policy positions of political actors, ed. Laver, M., 6675. London/New York: Routledge.
Laver, M. J., and Garry, J. 2000. Estimating policy positions from political texts. American Journal of Political Science 44(3): 619–34.
Laver, M., Benoit, K., and Garry, J. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(2): 311–31.
Little, R. J., and Rubin, D. B. 2002. Statistical analysis with missing data. 2nd ed. Hoboken, NJ: Wiley.
Lowe, W., Benoit, K., Mikhaylov, S., and Laver, M. 2011. Scaling policy preferences from coded political texts. Legislative Studies Quarterly 34(1): 123–55.
Martin, A. D., and Quinn, K. M. 2002. Dynamic ideal point estimation via Markov chain Monte Carlo for the U.S. Supreme Court, 1953–1999. Political Analysis 10(2): 134–53.
McDonald, R. P. 1999. Test theory: A unified treatment. Mahwah, NJ; London: Lawrence Erlbaum.
McKelvey, R. 1979. General conditions for global intransitivities in formal voting models. Econometrica 47(5): 1085–112.
Mikhaylov, S., Laver, M., and Benoit, K. R. 2012. Coder reliability and misclassification in the human coding of party manifestos. Political Analysis 20(1): 7891.
Poole, K. T., and Rosenthal, H. 1985. A spatial model for legislative roll call analysis. American Journal of Political Science 29(2): 357–84.
R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
Ripley, B. D. 1987. Stochastic simulation. Hoboken, NJ: Wiley.
Robert, C. P., and Casella, G. 2004. Monte Carlo statistical methods. 2nd ed. New York: Springer.
Slapin, J. B., and Proksch, S.-O. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52(3): 705–22.
Stegmueller, D. 2011. Apples and oranges? The problem of equivalence in comparative research. Political Analysis 19(4): 471–87.
Volkens, A., Lacewell, O., Regel, S., Schultze, H., and Werner, A. 2010. The Manifesto Data Collection. Berlin: Wissenschaftszentrum Berlin für Sozialforschung (WZB). https://manifestoproject.wzb.eu/ (accessed December 11, 2012).
Weakliem, D. L., and Heath, A. F. 1999. The secret life of class voting: Britain, France, and the United States since the 1930s. In The end of class politics? Class voting in comparative context, ed. Evans, G., 97136. Oxford: Oxford University Press.
Wei, G. C., and Tanner, M. A. 1990. A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association 85(411): 699704.
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Political Analysis
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