Bayesian Economics Through Numerical Methods

A Guide to Econometrics and Decision-Making with Prior Information

  • Jeffrey H. Dorfman

Table of contents

  1. Front Matter
    Pages i-vii
  2. Introduction

    1. Pages 1-3
  3. Theory and Basics

  4. Applications in Econometrics

  5. Applications to Economic Decision Making

    1. Front Matter
      Pages 87-87
  6. Back Matter
    Pages 97-110

About this book

Introduction

The aim of this book is to provide researchers in economics, finance, and statistics with an up-to-date introduction to applying Bayesian techniques to empirical studies. It covers the full range of the new numerical techniques which have been developed over the last thirty years, notably: 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. The book includes applications drawn from a variety of different fields within economics and also provides a quick overview to 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 research topic.

Keywords

bayesian statistics cointegration decision making decision theory econometrics economic theory economics forecasting integration statistics

Authors and affiliations

  • Jeffrey H. Dorfman
    • 1
  1. 1.Department of Agricultural and Applied EconomicsUniversity of GeorgiaAthensUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b97676
  • Copyright Information Springer-Verlag New York, Inc. 1997
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98233-5
  • Online ISBN 978-0-387-22635-4
  • About this book