Bayesian Model Comparison by Ivan Jeliazkov

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Summary

This volume of Advances in Econometrics 34 focusses on Bayesian model comparison. It reflects the recent progress in model building and evaluation that has been achieved in the Bayesian paradigm and provides new state-of-the-art techniques, methodology, and findings that should stimulate future research.

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Bayesian Model Comparison by Ivan Jeliazkov

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.
SKU Unavailable
ISBN 13 9781784411855
ISBN 10 178441185X
Title Bayesian Model Comparison
Author Ivan Jeliazkov
Series Advances In Econometrics
Condition Unavailable
Binding Type Hardback
Publisher Emerald Publishing Limited
Year published 2014-11-21
Number of pages 390
Cover note Book picture is for illustrative purposes only, actual binding, cover or edition may vary.