A Bayesian Perspective on the Maximum Score Problem
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917812648280064 |
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| author | Walker, Christopher D. |
| author_facet | Walker, Christopher D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17153 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Bayesian Perspective on the Maximum Score Problem Walker, Christopher D. Econometrics 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. |
| title | A Bayesian Perspective on the Maximum Score Problem |
| topic | Econometrics |
| url | https://arxiv.org/abs/2410.17153 |