Bayesian Econometric Methods / Edition 1

Bayesian Econometric Methods / Edition 1

by Gary Koop, Dale J. Poirier, Justin L. Tobias
     
 

ISBN-10: 0521671736

ISBN-13: 9780521671736

Pub. Date: 01/28/2007

Publisher: Cambridge University Press

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

Overview

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.

Product Details

ISBN-13:
9780521671736
Publisher:
Cambridge University Press
Publication date:
01/28/2007
Series:
Econometric Exercises Series
Edition description:
New Edition
Pages:
380
Product dimensions:
6.85(w) x 9.76(h) x 0.83(d)

Related Subjects

Table of Contents

Preface; 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 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; Appendix; Index.

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