Bayesian econometric methods
Material type:
TextPublication details: New York Cambridge University Press 2020 Edition: 2ndDescription: xxiii, 466 pISBN: 9781108437493Subject(s): Bayesian statistical decision theory | EconometricsDDC classification: 330.01519542 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
| 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 |
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
There are no comments on this title.