A Novel Class of Unfolding Models for Binary Preference Data

TL;DR

This paper introduces a new class of spatial voting models for binary preference data that are more flexible than existing models, allowing for better estimation of preferences in political settings.

Contribution

The paper presents a novel class of unfolding models capable of handling both monotonic and non-monotonic responses, improving preference estimation accuracy.

Findings

Models outperform existing alternatives in complexity-adjusted performance.

Preferences estimated by new models align more closely with perceived ideological positions.

Applications to U.S. legislators and justices demonstrate practical effectiveness.

Abstract

We develop 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 also that the additional flexibility leads to preferences' estimates that are closer matches to the perceived ideological positions of legislators and justices.