Researchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. Topics covered in the book include the regression model (and variants applicable for use with panel data), time series models, models for qualitative or censored data, nonparametric methods and Bayesian model averaging. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.
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Bayesian Econometrics introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. It includes numerous numerical examples and topics covered in the book include:
- the regression model (and variants applicable for use with panel data
- time series models
- models for qualitative or censored data
- nonparametric methods and Bayesian model averaging.
A website containing computer programs and data sets to help the student develop the computational skills of modern Bayesian econometrics can be found at: www.wiley.co.uk/koopbayesian
About the Author
Gary Koop is Professor of Economics at the University of Glasgow.
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Most Helpful Customer Reviews
2 of 2 people found the following review helpful:
5.0 out of 5 stars
Best Bayes Econ,
This review is from: Bayesian Econometrics (Paperback)
The best bayes econ there is. In my opinion. Good balance of prose and math.
10 of 17 people found the following review helpful:
5.0 out of 5 stars
Great book,
By A Customer
This review is from: Bayesian Econometrics (Paperback)
This is GREAT book which explains complicated concept in a very plain manner. The computer program (MATLAB) they posted on the web is also crearily written and very organized. I highly recommend this book for anyone who are interested in Bayesian Econometrics.
2 of 9 people found the following review helpful:
3.0 out of 5 stars
Lost in the wilderness of his formulations,
This review is from: Bayesian Econometrics (Paperback)
This could have been a great book. But, it is lost in the wilderness of its own formulations. It lacks focus.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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posterior simulator, being small projects, individual effects model, standard textbook questions, frequentist econometrics, partial linear model, elicit informative priors, nonparametric regression line, local level model, candidate generating density, prior sensitivity analysis, posterior results, posterior model probabilities, posterior simulation, latent data, local linear trend model, numerical standard errors, stochastic frontier model, multinomial probit model, taking random draws, house price data, nested model comparison, natural conjugate, posterior properties, candidate generating densities Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Random Walk Chain Metropolis-Hastings, Mean Standard Deviation, Independence Chain Metropolis-Hastings, Theoretical Exercises, Computer-Based Exercises Remember, Histogram of Posterior Means of Alpha, Econometrics Toolbox, Non-equipment Investment, Repeat Steps, Very Bad New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
posterior simulator, being small projects, individual effects model, standard textbook questions, frequentist econometrics, partial linear model, elicit informative priors, nonparametric regression line, local level model, candidate generating density, prior sensitivity analysis, posterior results, posterior model probabilities, posterior simulation, latent data, local linear trend model, numerical standard errors, stochastic frontier model, multinomial probit model, taking random draws, house price data, nested model comparison, natural conjugate, posterior properties, candidate generating densities Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Random Walk Chain Metropolis-Hastings, Mean Standard Deviation, Independence Chain Metropolis-Hastings, Theoretical Exercises, Computer-Based Exercises Remember, Histogram of Posterior Means of Alpha, Econometrics Toolbox, Non-equipment Investment, Repeat Steps, Very Bad New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
7
books
cite this book:
See all 7 books citing this book
- 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
See all 7 books citing this book
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