Bayesian economics through numerical methods : a guide to econometrics and decision-making with prior information /
by Dorfman, Jeffrey H.
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BookPublisher: New York : Springer, c1997Description: vii, 110 p. : ill. ; 25 cm.ISBN: 0387982337 (hc : alk. paper).Subject(s): Econometrics | Bayesian statistical decision theory| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
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Dhaka University Library General Stacks | Non Fiction | 330.015195 DOB (Browse shelf) | 1 | Available | 387625 | |
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Dhaka University Library General Stacks | Non Fiction | 330.015195 DOB (Browse shelf) | 2 | Available | 387626 |
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| 330.015195 DAF Foundations of mathematical & computational economics / | 330.015195 DAF Foundations of mathematical & computational economics / | 330.015195 DOB Bayesian economics through numerical methods : | 330.015195 DOB Bayesian economics through numerical methods : | 330.015195 ENA Applied econometric time series / | 330.015195 ENA Applied econometric time series / | 330.015195 FAC Fact and fiction in economics : |
Includes bibliographical references (p. 97-107) and index.
Ch. 1. Introduction -- Ch. 2. A Quick Course in Bayesian Statistics and Decision Theory -- Ch. 3. New Advances in Numerical Bayesian Techniques -- Ch. 4. Imposing Economic Theory -- Ch. 5. Studying Parameters of Interest -- Ch. 6. Unit Root and Cointegration Tests -- Ch. 7. Model Specification Uncertainty -- Ch. 8. Forecasting -- Ch. 9. More Realistic Models Through Numerical Methods -- Ch. 10. Decision Theory Applications.
The aim of this book is to provide researchers in economics, finance, and statistics with an up-to-date introduction to the application of Bayesian techniques to empirical studies. It covers the full range of the new numerical techniques that have been developed over the last thirty years, notably: Monte Carlo sampling, antithetic replication, importance sampling, and Gibbs sampling.
The result is a book that 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.
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