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Bayesian Econometric Methods Joshua Chan Gary Koop Unread Slight Cover Shelfwear
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eBay-Artikelnr.:146447919942
Artikelmerkmale
- Artikelzustand
- Sehr gut
- Hinweise des Verkäufers
- “Unread. No marks. Slight shelfwear to cover. From non-smoking, pet-free household.”
- Book Title
- Bayesian Econometric Methods
- ISBN
- 9781108437493
Über dieses Produkt
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1108437494
ISBN-13
9781108437493
eBay Product ID (ePID)
13038716716
Product Key Features
Number of Pages
486 Pages
Publication Name
Bayesian Econometric Methods
Language
English
Subject
Econometrics, Probability & Statistics / Bayesian Analysis
Publication Year
2019
Features
Revised
Type
Textbook
Subject Area
Mathematics, Business & Economics
Series
Econometric Exercises Ser.
Format
Trade Paperback
Dimensions
Item Height
0.9 in
Item Weight
35 Oz
Item Length
9.7 in
Item Width
6.9 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
LCCN
2019-013617
Reviews
'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
Dewey Edition
23
Series Volume Number
Series Number 7
Illustrated
Yes
Dewey Decimal
330.01519542
Edition Description
Revised edition
Table Of Content
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.
Synopsis
The second edition of Bayesian Econometric Methods illustrates Bayesian theory and application through a series of exercises, complete with solutions to those exercises and computer code. The book is suitable for graduate students in statistics, economics, finance and other disciplines., 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(R) 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., 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.
LC Classification Number
HB139.K6359 2019
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