A Bayesian Analysis of Tree Structure Specification in Nested Logit Models

This paper adopts a Bayesian approach to the problem of tree structure specification in nested logit models. I use the Laplace approximation and Reversible Jump Markov Chain Monte Carlo (RJMCMC) to estimate marginal likelihoods in both a simulated and a travel mode choice data set. I find that the Laplace approximation is remarkably accurate, and that model selection is invariant to prior specification

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Year of publication:	2007
Authors:	Verlinda, Jeremy A.
Publisher:	[S.l.] : SSRN
Subject:	Logit-Modell \| Logit model \| Mikroökonometrie \| Microeconometrics \| Monte-Carlo-Simulation \| Monte Carlo simulation \| Theorie \| Theory
Description of contents:	Abstract [papers.ssrn.com]

Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Economics Letters, Vol. 87, No. 1 , pp. 67-73, April 2005 Volltext nicht verfügbar
Classification:	C11 - Bayesian Analysis ; C25 - Discrete Regression and Qualitative Choice Models
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014052515