A Bayesian Perspective on the Maximum Score Problem

2024/10/22 by Christopher D. Walker, Walker, Christopher D. · 1 voice
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Advanced Statistical Process Monitoring #Econometrics (econ.EM) #FOS: Economics and business #Statistical Methods in Clinical Trials #econ.EM

paper · pdf · doi:10.48550/arxiv.2410.17153

openalex publication_date 2024/10/22 · arxiv published 2024/10/22 · arxiv updated 2024/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a Bayesian inference framework for a linear index threshold-crossing binary choice model that satisfies a median independence restriction. The key idea is that the model is observationally equivalent to a probit model with nonparametric heteroskedasticity. Consequently, Gibbs sampling techniques from Albert and Chib (1993) and Chib and Greenberg (2013) lead to a computationally attractive Bayesian inference procedure in which a Gaussian process forms a conditionally conjugate prior for the natural logarithm of the skedastic function.

Discussions

• During this semester I did my mixtures of normal approach to dealing with parametric SV models (the paper became Kim, Shephard and Chib (1998)). Recent use of that technique in microeconometrics is th [bsky, 0 points, 0 comments]

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