Bayesian Inference in Dynamic Econometric Models (Advanced Texts in Econometrics)

by Luc Bauwens, Michel Lubrano, and Jean-Francois 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.
  • ISBN10 0198773137
  • ISBN13 9780198773139
  • Publish Date 6 January 2000
  • Publish Status Active
  • Publish Country GB
  • Imprint Oxford University Press
  • Format Paperback
  • Pages 366
  • Language English