A new book in the Econometric Exercises series, this volume 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.
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Editorial Reviews
Review
"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 University
"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
Pravin K. Trivedi, Indiana University
"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
Book Description
This book aims to teach Bayesian econometrics by providing a wide range of solved exercises. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics.
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2 of 2 people found the following review helpful:
4.0 out of 5 stars
A pratical reference book,
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This review is from: Bayesian Econometric Methods (Econometric Exercises) (Paperback)
One good thing about this book is that there're lots of Bayesian model examples that you can search for. So, it's super pratical! On the other hand, if you want to dig deeply the Bayesian theories, then you need another kind of textbook.
2 of 3 people found the following review helpful:
4.0 out of 5 stars
Mathy but good.,
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This review is from: Bayesian Econometric Methods (Econometric Exercises) (Paperback)
I was looking for a little more explanation - but this is good. It is a little mathy, and I like other bayes econ books a little better. If you have some extra cash and are building a library - good book. If you need one text - others might be better.
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Key Phrases - Statistically Improbable Phrases (SIPs):
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posterior simulator, prior sensitivity analysis, multivariate normal analysis, importance sampling results, unknown changepoint, normal linear regression model, posterior probability content, posterior simulation, component indicator variables, conditional posterior mean, posterior results, latent data, numerical standard errors, reparameterized model, posterior conditionals, importance sampling weights, linex loss, expected posterior loss, prior hyperparameters, posterior properties, inverted gamma density, log marginal likelihood, find the posterior distribution, lagged autocorrelations, hyperparameter values Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Appendix Definition, Mean Std, Consider Exercise, Appendix Theorem, Find the Bayesian, The Matlab, Mean Post, Solution Let, Solution Consider, Consider the Bernoulli, Metropolis Hastings, Bruno de Finetti, Mean Standard Deviation, Parameter Post, Repeat Steps, Solution According, Solution Exercise, Solution First, Solution From Exercise, Tierney Kadane New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
posterior simulator, prior sensitivity analysis, multivariate normal analysis, importance sampling results, unknown changepoint, normal linear regression model, posterior probability content, posterior simulation, component indicator variables, conditional posterior mean, posterior results, latent data, numerical standard errors, reparameterized model, posterior conditionals, importance sampling weights, linex loss, expected posterior loss, prior hyperparameters, posterior properties, inverted gamma density, log marginal likelihood, find the posterior distribution, lagged autocorrelations, hyperparameter values Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Appendix Definition, Mean Std, Consider Exercise, Appendix Theorem, Find the Bayesian, The Matlab, Mean Post, Solution Let, Solution Consider, Consider the Bernoulli, Metropolis Hastings, Bruno de Finetti, Mean Standard Deviation, Parameter Post, Repeat Steps, Solution According, Solution Exercise, Solution First, Solution From Exercise, Tierney Kadane New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 37
books:
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7
books
cite this book:
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- Handbook of Econometrics, Volume 5 by J.J. Heckman in Back Matter (1), and Back Matter (2)
- Statistical Decision Theory: Foundations, Concepts, and Methods (Springer Series in Statistics) by J.O. Berger in Back Matter (1), and Back Matter (2)
- Continuous Univariate Distributions, Vol. 1 (Wiley Series in Probability and Statistics) by Norman L. Johnson in Back Matter (1), and Back Matter (2)
- Intermediate Statistics and Econometrics: A Comparative Approach by Dale J. Poirier in Front Matter, and Back Matter
- The Likelihood Principle (Ims Lecture Notes-Monograph : Vol 6) by James O. Berger in Back Matter
See all 37 books this book cites
- Matrix Algebra (Econometric Exercises) by Karim M. Abadir in Front Matter
- Guide to Econometrics by Peter Kennedy in Back Matter
- Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics) by Professor Peter Congdon on page 109
- An Introduction to Efficiency and Productivity Analysis by Timothy J. Coelli in Back Matter
- Simulation-based Inference in Econometrics: Methods and Applications by Roberto Mariano on page 297
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