A Bayesian Perspective on the Maximum Score Problem

Christopher D. Walker

Papers from arXiv.org

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.

Date: 2024-10
New Economics Papers: this item is included in nep-dcm and nep-ecm
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