Bayesian Model Comparison (Hardcover)

The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian thresholding methods in hierarchical graphical models, adaptive reversible jump MCMC, LASSO estimators, parameter expansion algorithms, the implementation of parameter and non-parameter-based approaches to variable selection, a survey of key results in objective Bayesian model selection methodology, and a careful look at the modeling of endogeneity in discrete data settings. Important contemporary questions are examined in applications in macroeconomics, finance, banking, labor economics, industrial organization, and transportation, among others, in which model uncertainty is a central consideration.

General

Imprint:	Emerald Publishing Limited
Country of origin:	United Kingdom
Series:	Advances in Econometrics
Release date:	November 2014
First published:	2014
Editors:	Ivan Jeliazkov • Dale J. Poirier
Dimensions:	229 x 152 x 36mm (L x W x T)
Format:	Hardcover
Pages:	390
ISBN-13:	978-1-78441-185-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Economics > Econometrics > General Promotions
LSN:	1-78441-185-X
Barcode:	9781784411855

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