A broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing.

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.

Introduction Part I: Principles 1. Bayesian Aspects of Treatment Choice 2. Exchangeability, Representation Theorems, and Subjectivity Part II: Methods 3. Bayesian Inference for Time Series State Space Models, Paolo Giordani 4. Flexible and Nonparametric Modelling 5. Introduction to Simulation and MCMC Methods Part III: Applications 6. Bayesian Methods in Microeconometrics 7. Bayesian Macroeconometrics 8. Bayesian Applications in Marketing 9. Bayesian Econometrics in Finance