Del i serien Advanced Texts in Econometrics

Bayesian Inference in Dynamic Econometric Models

Inbunden, Engelska, 2000

AvLuc Bauwens,Michel Lubrano,Jean-François Richard

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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.

Produktinformation

Utgivningsdatum2000-01-06
Mått163 x 242 x 24 mm
Vikt673 g
FormatInbunden
SpråkEngelska
SerieAdvanced Texts in Econometrics
Antal sidor366
FörlagOUP OXFORD
ISBN9780198773122

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Luc Bauwens is currently Professor of Economics at the Université catholique de Louvain, where he has been co-director of the Center for Operations Research and Econometrics (CORE) from 1992 to 1998. He has previously been a lecturer at Ecole des Hautes Etudes en Sciences Sociales (EHESS), France, at Facultés universitaires catholiques de Mons (FUCAM), Belgium, and a consultant at the World Bank, Washington DC. His research interests cover Bayesian inference, time series methods, simulation and numerical methods in econometrics, as well as empirical finance and international trade.Michel Lubrano is Directeur de Recherche at CNRS, part of GREQAM in Marseille.Jean-François Richard is University Professor of Economics at the University of Pittsburgh.

Chapter 1: Decision Theory and Bayesian Inference ; Chapter 2: Bayesian Statistics and Linear Regression ; Chapter 3: Methods of Numerical Integration ; Chapter 4: Prior Densities for the Regression Model ; Chapter 5: Dynamic Regression Models ; Chapter 6: Bayesian Unit Roots ; Chapter 7: Heteroskedasticity and ARCH ; Chapter 8: Nonlinear Tome Series Models ; Chapter 9: Systems of Equations ; Appendix A: Probability Distributions ; Appendix B: Generating Random Numbers