scholarly journals The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence

1994 ◽  
Vol 76 (4) ◽  
pp. 648 ◽  
Author(s):  
Michael P. Keane ◽  
Kenneth I. Wolpin
Keyword(s):  
Monte Carlo ◽  
Dynamic Programming ◽  
Discrete Choice ◽  
Programming Models

scholarly journals A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models

2012 ◽  
Vol 10 (2) ◽  
pp. 151-196 ◽  
Author(s):  
Andrew T. Ching ◽  
Susumu Imai ◽  
Masakazu Ishihara ◽  
Neelam Jain
Keyword(s):  
Dynamic Programming ◽  
Bayesian Estimation ◽  
Discrete Choice ◽  
Programming Models

Estimation of Discrete Choice Dynamic Programming Models

2017 ◽  
Vol 69 (1) ◽  
pp. 28-58 ◽  
Author(s):  
Hiroyuki Kasahara ◽  
Katsumi Shimotsu
Keyword(s):  
Dynamic Programming ◽  
Discrete Choice ◽  
Programming Models

scholarly journals Measuring hospital spatial wingspan by using a discrete choice model with utility-threshold

2018 ◽  
Author(s):  
Saley Issa ◽  
Ribatet Mathieu ◽  
Molinari Nicolas
Keyword(s):  
Monte Carlo ◽  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Models ◽  
Mcmc Methods ◽  
Hospital Competition ◽  
Policy Makers ◽  
New Approach ◽  
Asthma Patients ◽  
The One

AbstractPolicy makers increasingly rely on hospital competition to incentivize patients to choose high-value care. Travel distance is one of the most important drivers of patients’ decision. The paper presents a method to numerically measure, for a given hospital, the distance beyond which no patient is expected to choose the hospital for treatment by using a new approach in discrete choice models. To illustrate, we compared 3 hospitals attractiveness related to this distance for asthma patients admissions in 2009 in Hérault (France), showing, as expected, CHU Montpellier is the one with the most important spatial wingspan. For estimation, Monte Carlo Markov Chain (MCMC) methods are used.

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