Bayesian Econometric Methods (Google eBook)

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Cambridge University Press, Jan 15, 2007 - Business & Economics - 380 pages
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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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Contents

The subjective interpretation of probability
1
Point estimation
29
Interval estimation
51
7Prediction 71
91
The linear regression model
107
Hierarchical models
169
The linear regression model with general covariance matrix
191
Mixture models
253
Bayesian model averaging and selection
281
Some stationary time series models
297
Some nonstationary time series models
319
Appendix 335
353
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Page 1 - Theorem provides the key to the ways in which beliefs should fit together in the light of changing evidence. The goal, in effect, is to establish rules and procedures for individuals concerned with disciplined uncertainty accounting. The theory is not descriptive, in the sense of claiming to model actual behavior. Rather, it is prescriptive, in the sense of saying "if you wish to avoid the possibility of these undesirable consequences you must act in the following way.
Page 1 - ... inconsistencies. The theory establishes that expected utility maximization provides the basis for rational decision making and that Bayes' Theorem provides the key to the ways in which beliefs should fit together in the light of changing evidence. The goal, in effect, is to establish rules...

About the author (2007)

Gary Koop is Professor of Economics at the University of Strathclyde. He has published numerous articles in Bayesian econometrics and statistics in journals such as Journal of Econometrics, Journal of the American Statistical Association and the Journal of Business and Economic Statistics. He is an associate editor for several journals, including Journal of Econometrics and Journal of Applied Econometrics. He is the author of the books Bayesian Econometrics, Analysis of Economic Data and Analysis of Financial Data.

Dale J. Poirier is Professor of Economics at the University of California, Irvine. He is a Fellow of the Econometric Society, the American Statistical Association, and the Journal of Econometrics. He has been on the Editorial Boards of the Journal of Econometrics, Econometric Theory, and was the founding editor of Econometric Reviews. His professional activities have been numerous, and he has held elected positions in the American Statistical Association and the International Society for Bayesian Analysis. Previous books include Intermediate Statistics and Econometrics: A Comparative Approach and The Econometrics of Structural Change.

Justin L. Tobias is Associate Professor of Economics, Iowa State University, and has also served as an Assistant/Associate Professor of Economics at the University of California, Irvine. Professor Tobias has authored numerous articles in leading journals, including the International Economic Review, Journal of Applied Econometrics, Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics.

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