Bayesian econometric methods

By:

Chan, Joshua

Contributor(s):

Material type: Text

TextPublication details: Cambridge University Press New York 2020Edition: 2ndDescription: xxiii, 466 pISBN:

9781108437493

Subject(s):

DDC classification:

330.01519542 CHA

Summary: 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. Offers an update to the first edition by adding extensive coverage of macroeconomic models Provides additional exercises to aid researchers new to MCMC with understanding the methods MATLAB® computer programs are included on the website accompanying the text

List(s) this item appears in: Public Policy & General Management

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Public Policy & General Management	330.01519542 CHA (Browse shelf(Opens below))	1	Available		002765

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Table of Contents
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.

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.

Offers an update to the first edition by adding extensive coverage of macroeconomic models
Provides additional exercises to aid researchers new to MCMC with understanding the methods
MATLAB® computer programs are included on the website accompanying the text

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