Bayesian Inference in Dynamic Econometric Models
by Luc Bauwens, Michel Lubrano, Jean-François Richard
This book covers the principles and tools of Bayesian inference in econometrics. Bayesian inference is a branch of statistics that integrates explicitly both data and prior information in model building, estimation and evaluation. The book shows how to use Bayesian methods in models suited to the analysis of macroeconomic and financial time series
Hardcover
English
Brand New
Publisher Description
This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers abroad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It containsalso an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.
Table of Contents
Chapter 1: Decision Theory and Bayesian InferenceChapter 2: Bayesian Statistics and Linear RegressionChapter 3: Methods of Numerical IntegrationChapter 4: Prior Densities for the Regression ModelChapter 5: Dynamic Regression ModelsChapter 6: Bayesian Unit RootsChapter 7: Heteroskedasticity and ARCHChapter 8: Nonlinear Tome Series ModelsChapter 9: Systems of EquationsAppendix A: Probability DistributionsAppendix B: Generating Random Numbers
Review
`it can serve as a useful textbook for advanced undergraduate or graduate courses in either time series analysis or econometrics.'Paul Goodwin, International Journal of Forecasting, 2000`presents a comprehensive review of dynamic econometric models from a Bayesian perspective ... four insightful introductory chapters ... provide a valuable synthesis of current ideas and their applications to parameter estimation'Paul Goodwin, International Journal of Forecasting, 2000
Long Description
This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a
broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples
illustrate the methods.
Review Text
`it can serve as a useful textbook for advanced undergraduate or graduate courses in either time series analysis or econometrics.'
Paul Goodwin, International Journal of Forecasting, 2000
`presents a comprehensive review of dynamic econometric models from a Bayesian perspective ... four insightful introductory chapters ... provide a valuable synthesis of current ideas and their applications to parameter estimation'
Paul Goodwin, International Journal of Forecasting, 2000
Review Quote
'presents a comprehensive review of dynamic econometric models from aBayesian perspective ... four insightful introductory chapters ... provide avaluable synthesis of current ideas and their applications to parameterestimation'Paul Goodwin, International Journal of Forecasting, 2000
Feature
Comprehensive, up-to-date coverage of the field
Includes extensive examples of economic applications
Details
