A Bayesian Perspective on the Maximum Score Problem
Econometrics
2024-10-23 v1
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.
Cite
@article{arxiv.2410.17153,
title = {A Bayesian Perspective on the Maximum Score Problem},
author = {Christopher D. Walker},
journal= {arXiv preprint arXiv:2410.17153},
year = {2024}
}