Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility models, ARCH, GARCH, and vector autoregressive models. The authors have also added many new exercises related to Gibbs sampling and Markov Chain Monte Carlo (MCMC) methods. The text includes regression-based and hierarchical specifications, models based upon latent variable representations, and mixture and time series specifications. MCMC methods are discussed and illustrated in detail - from introductory applications to those at the current research frontier -?and MATLAB® computer programs are provided on the website accompanying the text. Suitable for graduate study in economics, the text should also be of interest to students studying statistics, finance, marketing, and agricultural economics.

Illustrates Bayesian theory and application through a series of exercises in question and answer format.

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 random variate generation and posterior simulation 12. Posterior simulation via Markov chain Monte Carlo 13. Hierarchical models 14. Latent variable models 15. Mixture models 16. Bayesian methods for model comparison, selection and big data 17. Univariate time series methods 18. State space and unobserved components models 19. Time series models for volatility 20. Multivariate time series methods Appendix Bibliography Index