Bayesian Econometric Methods
$50.00 ( ) USD
Part of Econometric Exercises
- Gary Koop, University of Strathclyde
- Dale J. Poirier, University of California, Irvine
- Justin L. Tobias, Iowa State University
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This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter half of the book contains exercises that show how these theoretical and computational skills are combined in practice, to carry out Bayesian inference in a wide variety of models commonly used by econometricians. Aimed primarily at advanced undergraduate and graduate students studying econometrics, this book may also be useful for students studying finance, marketing, agricultural economics, business economics or, more generally, any field which uses statistics. The book also comes equipped with a supporting website containing all the relevant data sets and MATLAB computer programs for solving the computational exercises.Read more
- Text offers 173 extended problems and complete solutions in Bayesian econometrics for upper-level undergraduates and above
- May also be used in courses in statistics and applied mathematics; comes with extensive programs on accompanying website
- Senior authors are internationally renowned Bayesian analysts
Reviews & endorsements
"This is an excellent addition to a well conceived and motivated series. Written by three prolific and mature contributors to modern Bayesian econometrics, it is well organized, clear, concise, and comprehensive. Combined with its associated web site, which provides the related computer programs, it is complementary to currently available Bayesian econometrics texts and dramatically lowers the cost of learning and using modern Bayesian econometric methods."
Pravin K. Trivedi, Indiana UniversitySee more reviews
"Koop, Poirier and Tobias have constructed a set of exercises in Bayesian econometrics and exposited these and their solutions with the exceptional clarity and good sense that one associates with these authors. A number of these exercises are of interest in their own right and, taken together, they will all provide a valuable complement to the introductory texts in Bayesian econometrics that have recently appeared on the market."
Anthony Lancaster, Brown University
"For the econometrician new to Bayesian methods, both the narrative and the exercises in this volume will expand conceptual horizons and establish new ways of thinking about econometrics. For the novice practitioner, the exercises provide an accessible bridge from theory to application. Experienced Bayesian practitioners will enjoy and benefit from testing their mettle on the wide selection of models treated in the book. Instructors at all levels will find material here that enhances classroom and computer laboratory experience."
John Geweke, University of Iowa
"The book presents a concise and comprehensive narration of theory, computational techniques, and a range of applications related to Bayesian methods. Overall, this 350-page book covers a vast range of topics, presenting them in a clear and intuitive fashion that can help disseminate these techniques to a broad but technically savvy audience."
Anirban Basu, Journal of the American Statistical Association
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- Date Published: August 2007
- format: Adobe eBook Reader
- isbn: 9780511292422
- contains: 39 tables
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
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
8. Choice of prior
9. Asymptotic Bayes
10. The linear regression model
11. Basics of Bayesian computation
12. Hierarchical models
13. The linear regression model with general covariance matrix
14. Latent variable models
15. Mixture models
16. Bayesian model averaging and selection
17. Some stationary time series models
18. Some nonstationary time series models
Instructors have used or reviewed this title for the following courses
- Advanced Fund Management
- Advanced Regression Analysis
- Forecasting and Time Series Analysis
- Investment Analysis and Portfolio Theory
- Methodology and Statistics
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