Hostname: page-component-5db58dd55d-4jdj6 Total loading time: 0 Render date: 2026-05-25T21:33:50.274Z Has data issue: false hasContentIssue false
  • English
  • Français

Nonparametric Ideal-Point Estimation and Inference

Published online by Cambridge University Press:  08 March 2018

Alexander Tahk
Alexander Tahk*
Affiliation:
Assistant Professor, Department of Political Science, University of Wisconsin–Madison, North Hall Rm 110, 1050 Bascom Mall, Madison, WI 53706, USA. Email: atahk@wisc.edu
*
*Email: atahk@wisc.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Existing approaches to estimating ideal points offer no method for consistent estimation or inference without relying on strong parametric assumptions. In this paper, I introduce a nonparametric approach to ideal-point estimation and inference that goes beyond these limitations. I show that some inferences about the relative positions of two pairs of legislators can be made with minimal assumptions. This information can be combined across different possible choices of the pairs to provide estimates and perform hypothesis tests for all legislators without additional assumptions. I demonstrate the usefulness of these methods in two applications to Supreme Court data, one testing for ideological movement by a single justice and the other testing for multidimensional voting behavior in different decades.

Keywords

ideal-point estimationhypothesis testingnonparametric analysisnonparametric estimation

Information

Type
Articles
Information
Political Analysis , Volume 26 , Issue 2 , April 2018 , pp. 131 - 146
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

Author’s note: I thank R. Michael Alvarez, Bret Hanlon, Simon Jackman, Jeff Lewis, Nolan McCarty, and Keith Poole. All mistakes are my own. Open-source software for the method proposed in this article is available at github.com/atahk/npideal as a package for the statistical software R. Replication materials for all of the results in this article are provided in the online Dataverse archive associated with this article (Tahk 2017).

Contributing Editor: R. Michael Alvarez

References

Bailey, M. A. 2013. Is todays court the most conservative in sixty years? Challenges and opportunities in measuring judicial preferences. Journal of Politics 75(3):821–834.Google Scholar
Bonica, Adam. 2011 Ideology and Interests in American Politics. Ph.D. thesis, New York University.Google Scholar
Chiou, F.-Y., and Rothenberg, Lawrence S.. 2003. When pivotal politics meets partisan politics. American Journal of Political Science 47(3):503–522.Google Scholar
Clinton, J., Jackman, Simon D., and Rivers, Douglas. 2004a. The most liberal senator? Analyzing and interpreting congressional roll calls. Political Science and Politics 37(4):805–811.Google Scholar
Clinton, J., Jackman, Simon D., and Rivers, Douglas. 2004b. The statistical analysis of roll call data. American Political Science Review 98(2):355–370.Google Scholar
Covington, Cary R., and Bargen, A. A.. 2004. Comparing floor-dominated and party-dominated explanations of policy change in the House of Representatives. Journal of Politics 66(4):1069–1088.Google Scholar
Epstein, Lee, Martin, Andrew D., Quinn, Kevin M., and Segal, Jeffrey A.. 2007. Ideological drift among supreme court justices: Who, when, and how important? Northwestern University Law Review 101(4):1483–1541.Google Scholar
Gibbons, Jean Dickinson, and Chakraborti, Subhabrata. 2011. Nonparametric statistical inference. In International encyclopedia of statistical science , ed. Lovric, Miodrag. Heidelberg: Springer, pp. 977–979.Google Scholar
Heckman, James J., and Snyder, James M. Jr. 1997. Linear probability models of the demand for attributes with an empirical application to estimating the preferences of legislators. RAND Journal of Economics 28:S142–S189.Google Scholar
Ho, Daniel, and Quinn, Kevin M.. 2010. How not to lie with judicial votes: Misconceptions, measurement, and models. California Law Review 98(3):813–876.Google Scholar
Krehbiel, Keith, and Peskowitz, Zachary. 2015. Legislative organization and ideal-point bias. Journal of Theoretical Politics 27(4):673–703.Google Scholar
Lauderdale, Benjamin E., and Clark, Tom S.. 2012. The Supreme Court’s many median justices. American Political Science Review 106(4):847–866.Google Scholar
McCarty, Nolan, Poole, Keith T., and Rosenthal, Howard. 2001. The hunt for party discipline in Congress. American Political Science Review 95(3):673–687.Google Scholar
Mokken, Robert J. 1971. A theory and procedure of scale analysis: With applications in political research . Berlin: Walter de Gruyter.Google Scholar
Neyman, Jerzy, and Scott, Elizabeth L.. 1948. Consistent estimates based on partially consistent observations. Econometrica 16(1):1–32.Google Scholar
Poole, Keith T. 2000. Nonparametric unfolding of binary choice data. Political Analysis 8(3):211–237.Google Scholar
Poole, Keith T. 2001. The geometry of multidimensional quadratic utility in models of parliamentary roll call voting. Political Analysis 9(3):211–226.Google Scholar
Poole, Keith T. 2007. Changing minds? Not in Congress! Public Choice 131(3–4):435–451.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1991. Patterns of congressional voting. American Journal of Political Science 35(1):228–278.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A Political-Economic History of Roll Call Voting . Oxford: Oxford University Press.Google Scholar
Randles, Ronald H., Hettmansperger, Thomas P., and Casella, George. 2004. Introduction to the special issue: Nonparametric statistics. Statistical Science 19(4):561–561.Google Scholar
Roberts, Jason M., Smith, S. S., and Haptonstahl, S. R.. 2016. The dimensionality of congressional voting reconsidered. American Politics Research 44(5):1–22.Google Scholar
Rosenthal, Howard. 1992. The unidimensional Congress is not the result of selective gatekeeping. American Journal of Political Science 36(1):31–36.Google Scholar
Snyder, James M. Jr., and Groseclose, Tim. 2000. Estimating party influence in congressional roll-call voting. American Journal of Political Science 44(2):193–211.Google Scholar
Spaeth, Harold J., Epstein, Lee, Andrew D., Martin, Segal, Jeffrey A., Ruger, Theodore J., and Benesh, Sara C.. 2015 Supreme Court Database. Version 2015 Release 01. August 17. http://Supremecourtdatabase.org.Google Scholar
Tahk, Alexander. 2017. Replication data for: Nonparametric ideal-point estimation and inference. doi:10.7910/DVN/WIRN6R, Harvard Dataverse, V1, UNF:6:v+zAWAFABTnFjZGegcS2iA==.Google Scholar
Wolfowitz, Jacob. 1942. Additive partition functions and a class of statistical hypotheses. Annals of Mathematical Statistics 13(3):247–279.Google Scholar
Supplementary material: File

Tahk supplementary material

Tahk supplementary material 1

Download Tahk supplementary material(File)
File 509.4 KB