A dynamic state-space model of coded political texts

Political Analysis

Volumn 21, Issue 2, 2013, Pages 217-232

  Elff, Martin ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84876068872     PISSN: 10471987     EISSN: 14764989     Source Type: Journal    
DOI: 10.1093/pan/mps042     Document Type: Article

References (46)

1. Adams, J., and S. Merrill 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.
2. Agresti, A. 2002. Categorical data analysis. 2nd ed. New York: Wiley.
3. Alesina, A. 1988. Credibility and policy convergence in a two-party system with rational voters. American Economic Review 78(4):796-805.
4. Benoit, K., and M. Laver. 2006. Party policy in modern democracies. London/New York: Routledge.
5. Benoit, K., S. Mikhaylov, and M. Laver. 2009. Treating words as data with error: Uncertainty in text statements of policy positions. American Journal of Political Science 53(2):495-513.
6. Booth, J. G., and J. P. Hobert. 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.
7. Budge, I., and D. Farlie. 1983. Party competition: selective emphasis or direct confrontation? An alternative view with data. In Western European party systems continuity and change, eds. H. Daalder and P. Mair, 265-305. Beverly Hills, CA: Sage.
8. Budge, I., H.-D. Klingemann, A. Volkens, J. Bara, and E. Tanenbaum. 2001. Mapping policy preferences: Estimates for parties, electors, and governments 1945-98. Oxford: Oxford University Press.
9. Budge, I., D. Robertson, and D. Hearl. 1987. Ideology, strategy, and party change: Spatial analysis of post war election programs in 19 democracies. Cambridge, MA: Cambridge University Press.
10. Caffo, B. S., W. Jank, and G. L. Jones. 2005. Ascent-based Monte Carlo expectation-maximization. Journal of the Royal Statistical Society, Series B (Methodological) 67(2):235-51.
11. Clinton, J., S. Jackman, and D. Rivers. 2004. The statistical analysis of roll call data. American Political Science Review 98(2):355-70.
12. Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological) 39(1):1-38.
13. Downs, A. 1957. An economic theory of democracy. New York: Harper & Row.
14. Eddelbuettel, D., and R. François. 2011. Rcpp: Seamless R and C++ integration. Journal of Statistical Software 40(8):1-18.
15. Elff, M. 2009. Social divisions, party positions, and electoral behaviour. Electoral Studies 28(2):297-308.
16. 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].
17. Enelow, J. M., and M. Hinich. 1984. The spatial theory of voting: An introduction. Cambridge, MA: Cambridge University Press.
18. Enelow, J. M., and M. Hinich. 1990. Advances in the spatial theory of voting. Cambridge, MA: Cambridge University Press.
19. Evans, G., A. Heath, and C. Payne. 1999. Class: Labour as a catch-all party? In Critical elections: British parties and voters in long-term perspective, eds. G. Evans and P. Norris. London; Thousand Oakes, CA; New Delhi: Sage Publications.
20. Ezrow, L. 2007. The variance matters: How party systems represent the preferences of voters. Journal of Politics 69(1):182-92.
21. Fielding, S. 2003. The Labour party: Continuity and change in the making of "New" Labour. New York: Palgrave Macmillan.
22. Francois, R., D. Eddelbuettel, and D. Bates. 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).
23. Gentle, J. E. 2003. Random number generation and Monte Carlo methods. 2nd ed. New York: Springer.
24. Heath, A., R. Jowell, J. Curtice, G. Evans, J. Field, and S. Witherspoon. 1991. Understanding political change: The British voter 1964-1987. Oxford: Pergamon Press.
25. Hinich, M. J., and M. C. Munger. 1994. Ideology and the theory of political choice. Ann Arbor: University of Michigan Press.
26. Klingemann, H.-D., A. Volkens, J. Bara, I. Budge, and M. McDonald. 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.
27. Kollman, K., J. H. Miller, and S. E. Page. 1992a. Adaptive parties in spatial elections. American Political Science Review 86(4):929-37.
28. Kollman, K., J. H. Miller, and S. E. Page. 1992b. Political parties and electoral landscapes. British Journal of Political Science 28(1):139-58.
29. Laver, M. 2001. Position and salience in the policies of political actors. In Estimating the policy positions of political actors, ed. M. Laver, 66-75. London/New York: Routledge.
30. Laver, M. J., and J. Garry. 2000. Estimating policy positions from political texts. American Journal of Political Science 44(3):619-34.
31. Laver, M., K. Benoit, and J. Garry. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(2):311-31.
32. Little, R. J., and D. B. Rubin. 2002. Statistical analysis with missing data. 2nd ed. Hoboken, NJ: Wiley.
33. Lowe, W., K. Benoit, S. Mikhaylov, and M. Laver. 2011. Scaling policy preferences from coded political texts. Legislative Studies Quarterly 34(1):123-55.
34. Martin, A. D., and K. M. Quinn. 2002. Dynamic ideal point estimation via Markov chain Monte Carlo for the U.S. Supreme Court, 1953-1999. Political Analysis 10(2):134-53.
35. McDonald, R. P. 1999. Test theory: A unified treatment. Mahwah, NJ; London: Lawrence Erlbaum.
36. McKelvey, R. 1979. General conditions for global intransitivities in formal voting models. Econometrica 47(5):1085-112.
37. Mikhaylov, S., M. Laver, and K. R. Benoit. 2012. Coder reliability and misclassification in the human coding of party manifestos. Political Analysis 20(1):78-91.
38. Poole, K. T., and H. Rosenthal. 1985. A spatial model for legislative roll call analysis. American Journal of Political Science 29(2):357-84.
39. R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
40. Ripley, B. D. 1987. Stochastic simulation. Hoboken, NJ: Wiley.
41. Robert, C. P., and G. Casella. 2004. Monte Carlo statistical methods. 2nd ed. New York: Springer.
42. Slapin, J. B., and S.-O. Proksch. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52(3):705-22.
43. Stegmueller, D. 2011. Apples and oranges? The problem of equivalence in comparative research. Political Analysis 19(4):471-87.
44. Volkens, A., O. Lacewell, S. Regel, H. Schultze, and A. Werner. 2010. The Manifesto Data Collection. Berlin: Wissenschaftszentrum Berlin fü r Sozialforschung (WZB). https://manifestoproject.wzb.eu/ (accessed December 11, 2012).
45. Weakliem, D. L., and A. F. Heath. 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. G. Evans, 97-136. Oxford: Oxford University Press.
46. Wei, G. C., and M. A. Tanner. 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):699-704.