Bayesian Model Comparison (Advances in Econometrics #34) (Hardcover)

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Bayesian Model Comparison (Advances in Econometrics #34) (Hardcover)

By Ivan Jeliazkov (Editor), Dale J. Poirier (Editor)

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This is book number 34 in the Advances in Econometrics series.

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.
Product Details ISBN: 9781784411855
ISBN-10: 178441185X
Publisher: Emerald Group Publishing
Publication Date: November 21st, 2014
Pages: 390
Language: English
Series: Advances in Econometrics