Bayesian econometric methods have enjoyed an increase in popularity in recent years.
Econometricians, empirical economists, and policymakers are increasingly making use of
Bayesian methods.
This handbook is a single source for researchers and policymakers wanting
to learn about Bayesian methods in specialized fields, and for graduate students seeking
to make the final step from textbook learning to the research frontier.
It contains contributions by leading Bayesians on the latest developments in
their specific fields of expertise. The volume provides broad coverage of the application
of Bayesian econometrics in the major fields of economics and related disciplines,
including macroeconomics, microeconomics, finance, and marketing. It reviews the state of
the art in Bayesian econometric methodology, with chapters on posterior simulation and
Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized
tools used by Bayesian time series econometricians such as state space models and particle
filtering. It also includes chapters on Bayesian principles and methodology.
Table of Contents
Introduction, John Geweke, Gary Koop, and
Herman van Dijk
Part I: Principles
1. Bayesian Aspects of Treatment Choice, Gary Chamberlain
2. Exchangeability, Representation Theorems, and Subjectivity, Dale Poirier
Part II: Methods
3. Bayesian Inference for Time Series State Space Models, Paolo Giordani, Michael
Pitt, and Robert Kohn
4. Flexible and Nonparametric Modelling, Jim
Griffin, Fernando Quintana, and Mark Steel
5. Introduction to Simulation and MCMC Methods, Siddhartha Chib
Part III: Applications
6. Bayesian Methods in Microeconometrics, Mingliang
Li and Justin Tobias
7. Bayesian Macroeconometrics, Marco Del Negro and
Frank Schorfheide
8. Bayesian Applications in Marketing, Peter Rossi and Greg Allenby
9. Bayesian Econometrics in Finance, Eric Jacquier and Nicholas Polson
560 pages, Hardcover