Joshua Chan, Gary Koop, Dale J. Poirier, Justin L. TobiasCambridge University PressEdition: 2, 8/15/2019EAN 9781108423380, ISBN10: 1108423388Hardcover, 484 pages, 24.9 x 18.3 x 2.6 cmLanguage: EnglishBayesian 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 probability2. Bayesian inference3. Point estimation4. Frequentist properties of Bayesian estimators5. Interval estimation6. Hypothesis testing7. Prediction8. Choice of prior9. Asymptotic Bayes10. The linear regression model11. Basics of random variate generation and posterior simulation12. Posterior simulation via Markov chain Monte Carlo13. Hierarchical models14. Latent variable models15. Mixture models16. Bayesian methods for model comparison, selection and big data17. Univariate time series methods18. State space and unobserved components models19. Time series models for volatility20. Multivariate time series methodsAppendixBibliographyIndex.
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