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

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 ...

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Paperback (Softcover reprint of the original 1st ed. 1997)
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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.

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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: 6.14 (w) x 9.21 (h) x 0.26 (d)

Table of Contents

Theory and Basics.- A Quick Course in Bayesian Statistics and Decision Theory.- New Advances in Numerical Bayesian Techniques.- Applications in Econometrics.- Imposing Economic Theory.- Studying Parameters of Interest.- Unit Root and Cointegration Tests.- Model Specification Uncertainty.- Forecasting.- More Realistic Models Through Numerical Methods.- Applications to Economic Decision Making.- Decision Theory Applications.

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