- Paperback: 416 pages
- Publisher: Wiley-Blackwell; 1 edition (June 18, 2004)
- Language: English
- ISBN-10: 1405117206
- ISBN-13: 978-1405117203
- Product Dimensions: 6.8 x 0.9 x 9.7 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 5 customer reviews
- Amazon Best Sellers Rank: #1,028,956 in Books (See Top 100 in Books)
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Introduction to Modern Bayesian Econometrics 1st Edition
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“This book conveys the revolution in Bayesian statistics brought about by modern computing and simulation methods from a perspective that econometricians will find familiar. It works through the implications for econometric practice using practical examples and accessible computer software. Graduate students in economics will find it highly accessible. Practitioners steeped in classical econometric methods will find much that is new, exciting, and useful here as well.” John Geweke, University of Iowa
“Lancaster's text gives an impressive overview of the Bayesian point of view, and should prove a valuable resource to econometricians of all persuasions.” Werner Ploberger, University of Rochester
Almost two hundred and forty years ago, an English clergyman named Thomas Bayes developed a method to calculate the chances of uncertain events. While his method has extensive applications to the work of applied economists, it is only recent advances in computing that have made it possible to exploit the full power of the Bayesian way of doing applied economics.In this new and expanding area, Tony Lancasters text provides a comprehensive introduction to the Bayesian way of doing applied economics. Using clear explanations and practical illustrations and problems, the text presents innovative, computer-intensive ways for applied economists to use the Bayesian method.The Introduction emphasizes computation and the study of probability distributions by computer sampling, showing how these techniques can provide exact inferences about a wide range of econometric problems. Covering all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data, it also details causal inference and inference about structural econometric models. In addition, each chapter includes numerical and graphical examples and demonstrates their solutions using the S programming language and Bugs software.
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I found this book in the library and it is much better. It gives very clear explanations of the ideas, and lots of concrete worked examples. Props to the author.