Logit unfolding choice models for binary data

TL;DR

This paper introduces a novel binary choice model with heavy-tailed shocks, employing a Markov chain Monte Carlo algorithm that effectively fits voting data and allows for Bayesian hypothesis testing on latent constructs.

Contribution

The paper presents a new unfolding model for binary data with heavy-tailed shocks and a robust MCMC algorithm requiring minimal tuning, enabling advanced analysis of political voting data.

Findings

Model provides better fit to US House voting data.

Algorithm explores posterior distribution thoroughly.

Supports hypothesis testing on latent variables.

Abstract

Discrete choice models with non-monotonic response functions are important in many areas of application, especially political sciences and marketing. This paper describes a novel unfolding model for binary data that allows for heavy-tailed shocks to the underlying utilities. One of our key contributions is a Markov chain Monte Carlo algorithm that requires little or no parameter tuning, fully explores the support of the posterior distribution, and can be used to fit various extensions of our core model that involve (Bayesian) hypothesis testing on the latent construct. Our empirical evaluations of the model and the associated algorithm suggest that they provide better complexity-adjusted fit to voting data from the United States House of Representatives.