Description
Book SynopsisThe 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.
Table of ContentsAdaptive 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 Parameter Estimation Methods. Intrinsic Priors for Objective Bayesian Model Selection. Copyright page. Bayesian Model Comparison. List of Contributors. Preface. Advances in Econometrics. Bayesian Model Comparison. Demand Estimation with High-Dimensional Product Characteristics. Copula Analysis of Correlated Counts.
