Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo

We develop a Markov Chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametrization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples involving 3-level structures. The first example involves Social Security's disability determination process (Soc. Security Bull. 58 (1995)). The second one is taken from Amemiya and Shimono's (Econ. Stud. Q. 40 (1989)) model of labor supply bevavior of the aged. We applied a combination of various convergence criteria to ensure that the chain has converged to its target distribution

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Year of publication:	2002
Authors:	Lahiri, Kajal ; Gao, Jian
Publisher:	[S.l.] : SSRN
Subject:	Logit-Modell \| Logit model \| Markov-Kette \| Markov chain \| Theorie \| Theory \| Monte-Carlo-Simulation \| Monte Carlo simulation \| Bayes-Statistik \| Bayesian inference

Extent:	1 Online-Ressource (71 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 15, 2002 erstellt
Other identifiers:	10.2139/ssrn.317779 [DOI]
Classification:	C11 - Bayesian Analysis ; C25 - Discrete Regression and Qualitative Choice Models ; H55 - Social Security and Public Pensions ; I12 - Health Production: Nutrition, Mortality, Morbidity, Substance Abuse and Addiction, Disability, and Economic Behavior ; J14 - Economics of the Elderly
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014113986