History[ edit ] The ideas underlying Bayesian statistics were developed by Rev. Thomas Bayes during the 18th century and later expanded by Pierre-Simon Laplace. As early as , the potential of the Bayesian inference in econometrics was recognized by Jacob Marschak. Rothenberg , George Tiao , and Arnold Zellner.
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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. Written by three prolific and mature contributors to modern Bayesian econometrics, it is well organized, clear, concise, and comprehensive.
Combined with its associated web site, which provides the related computer programs, it is complementary to currently available Bayesian econometrics texts and dramatically lowers the cost of learning and using modern Bayesian econometric methods.
A number of these exercises are of interest in their own right and, taken together, they will all provide a valuable complement to the introductory texts in Bayesian econometrics that have recently appeared on the market. For the novice practitioner, the exercises provide an accessible bridge from theory to application. Experienced Bayesian practitioners will enjoy and benefit from testing their mettle on the wide selection of models treated in the book.
Instructors at all levels will find material here that enhances classroom and computer laboratory experience. Overall, this page book covers a vast range of topics, presenting them in a clear and intuitive fashion that can help disseminate these techniques to a broad but technically savvy audience.
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. Dale J. His professional activities have been numerous, and he has held elected positions in the American Statistical Association and the International Society for Bayesian Analysis.
Bayesian Econometric Methods by Gary Koop