Adaptive sequential posterior simulators for massively parallel computing environments / Garland Durham, John Geweke -- Model switching and model averaging in time-varying parameter regression models / Miguel Belmonte, Gary Koop -- Assessing Bayesian model comparison in small samples / Enrique Martínez-García, Mark A. Wynne -- Bayesian selection of systemic risk networks / Daniel Felix Ahelegbey, Paolo Giudici -- Parallel constrained Hamiltonian Monte Carlo for BEKK model comparison / Martin Burda -- Factor selection in dynamic hedge fund replication models : a Bayesian approach / Guillaume Weisang -- Determining the proper specification for endogenous covariates in discrete data settings / Angela Vossmeyer -- Variable selection in Bayesian models : using parameter estimation and non parameter estimation methods / Gail Blattenberger, Richard Fowles, Peter D. Loeb -- Intrinsic priors for objective Bayesian model selection / Elías Moreno, Luís Raúl Pericchi -- Demand estimation with high-dimensional product characteristics / Benjamin J. Gillen, Matthew Shum, Hyungsik Roger Moon -- Copula analysis of correlated counts / Esther Hee Lee.

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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