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A Bayesian analysis of tree structure specification in nested logit models [An article from: Economics Letters]
Book Details
Author(s)
J.A. Verlinda
Publisher
Elsevier
ISBN / ASIN
B000RR1UX2
ISBN-13
978B000RR1UX9
Availability
Available for download now
Sales Rank
#9,382,147
Marketplace
United States 🇺🇸
Description
This digital document is a journal article from Economics Letters, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
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.
Description:
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.
