Abstract:
This talk presents a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and that the additional flexibility leads to preferences' estimates that more closely match the perceived ideological positions of legislators and justices. The presentation is joint work with former postdoctoral scholar Rayleigh Lei, and it is based on the papers:
Lei, R., & Rodríguez, A. (2025). A novel class of unfolding models for binary preference data. Political Analysis, 33(1), 32-48. [https://doi.org/10.1017/pan.2024.11]
Lei, R., & Rodriguez, A. (2025). Logit unfolding choice models for binary data. Statistics and Computing, 35(2), 1-18. [https://link.springer.com/article/10.1007/s11222-025-10570-5]
Shi, S., Rodriguez, A. and Lei, R. (2025+). pumBayes: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data in R. Submitted to Journal of Statistical Software
Abel Rodriguez is a Professor in the Department of Statistics at the University of Washington.
