Bayesian Econometric Methods

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

Joshua Chan (Author), Gary Koop (Author), Dale J. Poirier (Author), Justin L. Tobias (Author)

9781108423380, Cambridge University Press

Hardback, published 15 August 2019

484 pages, 50 b/w illus. 48 tables
25.8 x 17.8 x 2.6 cm, 1.12 kg

'This is a clear, concise, and, above all, practical introduction to Bayesian econometrics. Graduate and advanced undergraduate students will find here a self-contained introduction to Bayesian theory, computation, and applied econometric modeling that can accompany them well into their studies.' William J. McCausland, Université de Montréal

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.

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.

Subject Areas: Economic statistics [KCHS], Econometrics [KCH], Macroeconomics [KCB], Economics [KC]