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Nonparametric Ideal-Point Estimation and Inference

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  • Tahk, Alexander

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.

Suggested Citation

  • Tahk, Alexander, 2018. "Nonparametric Ideal-Point Estimation and Inference," Political Analysis, Cambridge University Press, vol. 26(2), pages 131-146, April.
    • Handle: RePEc:cup:polals:v:26:y:2018:i:02:p:131-146_00
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    Cited by:

    1. Christopher Hare & Tzu-Ping Liu & Robert N. Lupton, 2018. "What Ordered Optimal Classification reveals about ideological structure, cleavages, and polarization in the American mass public," Public Choice, Springer, vol. 176(1), pages 57-78, July.
    2. Lerner, Joshua Y. & McCubbins, Mathew D. & Renberg, Kristen M., 2021. "The efficacy of measuring judicial ideal points: The mis-analogy of IRTs," International Review of Law and Economics, Elsevier, vol. 68(C).

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