Joshua Chan, Gary Koop, Dale J. Poirier

Bayesian Econometric Methods

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Econometric Exercises 7
• Verlag: Cambridge University Press
• 2., überarb. Aufl.
• Seitenzahl: 492
• Erscheinungstermin: 15. August 2019
• Englisch
• Abmessung: 244mm x 170mm x 26mm
• Gewicht: 990g
• ISBN-13: 9781108437493
• ISBN-10: 1108437494
• Artikelnr.: 56258832

Autorenporträt

Joshua Chan is Professor of Economics at Purdue University, Indiana. He is interested in building flexible models for large datasets and developing efficient estimation methods. His favorite applications include trend inflation estimation and macroeconomic forecasting. He has co-authored the textbook Statistical Modeling and Computation (2013).

Inhaltsangabe

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.

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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.

Rezensionen

'This volume invigorates the understanding and application of Bayesian econometrics with a uniquely constructive, hands-on approach. By moving seamlessly between theory, methods, and applications, it builds understanding and skills that will serve the novice Bayesian econometrician well, and synthesizes the subject for experienced Bayesian practitioners.' John Geweke, Charles R. Nelson Endowed Professor in Economics, University of Washington