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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

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.

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.

Foreword / Ivan Jeliazkov and Justin Tobias -- 1. A Semiparametric Stochastic Frontier Model with Correlated Effects / Gholamreza Hajargasht and William Griffiths -- 2. A Bayesian Stochastic Frontier Model with Endogenous Regressors: An Application to the Effect of Division of Labor in Japanese Water Supply Organizations / Eri Nakamura, Takuya Urakami and Kazuhiko Kakamu -- 3. An Alternate Parameterization for Bayesian Nonparametric / Semiparametric Regression / Joshua Chan and Justin Tobias -- 4. Variable Selection in Sparse Semiparametric Single Index Models / Jianghao Chu, Tae-Hwy Lee and Aman Ullah -- 5. Fully Nonparametric Bayesian Additive Regression Trees / Edward George, Prakash Laud, Brent Logan, Robert McCulloch and Rodney Sparapani -- 6. Bayesian A/B Inference / John Geweke -- 7. Scalable semiparametric inference for the means of heavy-tailed distributions / Hedibert Lopes, Matthew Taddy and Matthew Gardner -- 8. Estimation and Applications of Quantile Regression for Binary Longitudinal Data / Mohammad Arshad Rahman and Angela Vossmeyer -- 9. On Quantile Estimator in Volatility Model with Non-negative Error Density and Bayesian Perspective / Debajit Dutta, Subhra Sankar Dhar and Amit Mitra -- 10. Flexible Bayesian Quantile Regression in Ordinal Models / Mohammad Arshad Rahman and Shubham Karnawat -- 11. A Reaction / Dale Poirier.

1.The subjective interpretation of probability --2.Bayesian inference --3.Point estimation --4.Frequentist properties of bayesian estimators --5.Interval estimation --6.Hypothesis testing --7.Prediction --8.Choice of prior --9.Asymptotic bayes --10.The linear regression model --11.Basics of bayesian computation --12.Hierarchical models --13.The linear regression model with general covariance matrix --14.Latent variable models --15.Mixture models --16.Bayesian model averaging and selection --17.Some stationary time series models --18.Some nonstationary time series models.

This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter half of the book contains exercises that show how these theoretical and computational skills are combined in practice, to carry out Bayesian inference in a wide variety of models commonly used by econometricians. Aimed primarily at advanced undergraduate and graduate students studying econometrics, this book may also be useful for students studying finance, marketing, agricultural economics, business economics or, more generally, any field which uses statistics. The book also comes equipped with a supporting website containing all the relevant data sets and MATLAB computer programs for solving the computational exercises

"The past two decades have seen econometrics grow into a vast discipline. Many different branches of the subject now happily coexist with one another. These branches interweave econometric theory and empirical applications and bring econometric method to bear on a myriad of economic issues."