Bayesian Economics Through Numerical Methods
A Guide to Econometrics and Decision-Making with Prior Information
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Bayesian Economics Through Numerical Methods “
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.
Bibliographische Angaben
- Autor: Jeffrey H. Dorfman
- 2013, Softcover reprint of the original 1st ed. 1997, VIII, 110 Seiten, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1475771029
- ISBN-13: 9781475771022
Sprache:
Englisch
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