Bayesian Inference in Dynamic Econometric Models
Luc Bauwens, Michel Lubrano, and Jean-François Richard
Abstract
This book contains an up-to-date coverage of the last twenty years of 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 ... More
Keywords: Bayesian, econometrics, dynamic models, numerical integration, Markov Chain Monte Carlo methods, linear regression, non-linear models, autoregressive conditional heteroskedastic regressions, cointegrated vector autoregressive models, unit root inference
Bibliographic Information
| Print publication date: 2000 | Print ISBN-13: 9780198773122 |
| Published to Oxford Scholarship Online: September 2011 | DOI:10.1093/acprof:oso/9780198773122.001.0001 |
