Bayesian Model Comparison

Business & Finance, Economics, Econometrics, Nonfiction, Social & Cultural Studies, Political Science, Politics, Economic Policy
Cover of the book Bayesian Model Comparison by , Emerald Group Publishing Limited
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Author: ISBN: 9781784411848
Publisher: Emerald Group Publishing Limited Publication: November 21, 2014
Imprint: Emerald Group Publishing Limited Language: English
Author:
ISBN: 9781784411848
Publisher: Emerald Group Publishing Limited
Publication: November 21, 2014
Imprint: Emerald Group Publishing Limited
Language: English

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.

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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.

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