Description
Tools to improve decision making in an imperfect world
This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data.
The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including:
* Linear models and policy choices
* Modeling with latent variables and missing data
* Time series models and prediction
* Comparison and evaluation of models
The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets.
This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.
About the Author
JOHN GEWEKE, PHD, is Harlan McGregor Chair in Economic Theory and Professor of Economics and Statistics at the University of Iowa. He is an elected Fellow of the Econometric Society and the American Statistical Association, former President of the International Society for Bayesian Analysis, and coeditor of the Journal of Econometrics.
Reviews
"This book has the potentials to become a classic for teaching (Computational) Bayesian Econometrics...the book will be a valuable reference for all people working in the field of MCMC" (Stat Papers, October 2008)
Book Information
ISBN 9780471679325
Author John Geweke
Format Hardback
Page Count 320
Imprint Wiley-Interscience
Publisher John Wiley & Sons Inc
Weight(grams) 612g
Dimensions(mm) 239mm * 165mm * 19mm
This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data.
The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including:
* Linear models and policy choices
* Modeling with latent variables and missing data
* Time series models and prediction
* Comparison and evaluation of models
The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets.
This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.
About the Author
JOHN GEWEKE, PHD, is Harlan McGregor Chair in Economic Theory and Professor of Economics and Statistics at the University of Iowa. He is an elected Fellow of the Econometric Society and the American Statistical Association, former President of the International Society for Bayesian Analysis, and coeditor of the Journal of Econometrics.
Reviews
"This book has the potentials to become a classic for teaching (Computational) Bayesian Econometrics...the book will be a valuable reference for all people working in the field of MCMC" (Stat Papers, October 2008)
"Written by a recognized scholar...this book fills a void even though a number of recent titles have been published on a similar scope." (E-STREAMS, September 2007)
"I enjoyed reading [it]...and think it would make a great textbook for a Bayesian course at the graduate level in finance, business, marketing, and the social sciences...the book is also a great reference..." (Journal of the American Statistical Association, September 2006)
"This book is tailored for researchers-professionals who use econometrics and statistics in their research...is also an excellent textbook for graduate students in a broad range of fields." (Mathematical Reviews, 2006d)
Book Information
ISBN 9780471679325
Author John Geweke
Format Hardback
Page Count 320
Imprint Wiley-Interscience
Publisher John Wiley & Sons Inc
Weight(grams) 612g
Dimensions(mm) 239mm * 165mm * 19mm
