Bayesian model comparison / edited by Ivan Jeliazkov, Dale J. Poirier.

Includes bibliographical references.

Adaptive sequential posterior simulators for massively parallel computing environments -- Model switching and model averaging in time-varying parameter regression models -- Assessing Bayesian model comparison in small samples -- Bayesian selection of systemic risk networks -- Parallel constrained Hamiltonian Monte Carlo for Bekk model comparison -- Factor selection in dynamic hedge fund replication models: a Bayesian approach -- Determining the proper specification for endogenous covariates in discrete data settings -- Variable selection in Bayesian models: using parameter estimation and non paramter estimation methods -- Intrinsic priors for objective Bayesian model selection -- Demand estimation with high-dimensional product characteristics -- Copula analysis of correlated counts.

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This volume of Advances in econometrics is devoted to 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. 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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