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Bayesian Economics Through Numerical Methods: A Guide to Econometrics and Decision-Making with Prior Information
     

Bayesian Economics Through Numerical Methods: A Guide to Econometrics and Decision-Making with Prior Information

by Jeffrey H. Dorfman
 

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Providing researchers in economics, finance, and statistics with an up-to-date introduction to applying Bayesian techniques to empirical studies, this book covers the full range of the new numerical techniques which have been developed over the last thirty years. Notably, these are: Monte Carlo sampling, antithetic replication, importance sampling, and Gibbs

Overview

Providing researchers in economics, finance, and statistics with an up-to-date introduction to applying Bayesian techniques to empirical studies, this book covers the full range of the new numerical techniques which have been developed over the last thirty years. Notably, these are: Monte Carlo sampling, antithetic replication, importance sampling, and Gibbs sampling. The author covers both advances in theory and modern approaches to numerical and applied problems, and includes applications drawn from a variety of different fields within economics, while also providing a quick overview of the underlying statistical ideas of Bayesian thought. The result is a book which presents a roadmap of applied economic questions that can now be addressed empirically with Bayesian methods. Consequently, many researchers will find this a readily readable survey of this growing topic.

Product Details

ISBN-13:
9781475771022
Publisher:
Springer New York
Publication date:
12/31/2013
Edition description:
Softcover reprint of the original 1st ed. 1997
Pages:
110
Product dimensions:
6.10(w) x 9.25(h) x 0.01(d)

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