## Bayesian Inference in Dynamic Econometric ModelsThis 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. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

1 | |

Bayesian Statistics and Linear Regression | 35 |

Methods of Numerical Integration | 65 |

Prior Densities for the Regression Model | 94 |

Dynamic Regression Models | 129 |

Unit Root Inference | 158 |

Heteroscedasticity and ARCH | 197 |

NonLinear Time Series Models | 231 |

Systems of Equations | 265 |

Probability Distributions | 289 |

B Generating random numbers | 312 |

323 | |

340 | |

347 | |

### Other editions - View all

Bayesian Inference in Dynamic Econometric Models Luc Bauwens,Michel Lubrano,Jean François Richard Limited preview - 1999 |

Bayesian Inference in Dynamic Econometric Models Luc Bauwens,Michel Lubrano,Jean François Richard No preview available - 1999 |

### Common terms and phrases

algorithm analysis analytically apply ARCH models autocorrelation Bauwens and Lubrano Bayes Bayes factor Bayesian inference classical coefficients cointegrating conditional density conditionally conjugate prior consider constant term corresponding covariance defined deterministic Dijk draws Dreze dynamic econometrics equal equation estimator evaluated example factor flat prior follows formula GARCH Geweke Gibbs sampler given heteroscedasticity hypothesis importance function importance sampling independent initial condition introduced inverted gamma-2 Jeffreys kernel likelihood function linear regression linear regression model marginal density marginal likelihood marginal posterior matricvariate matrix method Monte Carlo natural conjugate prior non-informative prior non-linear model normal distribution numerical integration observations obtained poly-t posterior density posterior expectation posterior mean posterior moments posterior results predictive density prior density prior information probability problem random variable regime restrictions risk function Section simulation standard deviation stationarity Student density Student distribution Subsection sufficient statistics Theorem transformation unit root vector zero

### Popular passages

Page viii - Centre National de la Recherche Scientifique, Ecole des Hautes Etudes en Sciences Sociales, Groupe de Recherche en Economic Quantitative et Econometric, Ministere des Affaires Etrange"res (in France), and the European Commission through the Human Capital and Mobility Programme network 'Simulation Methods in Econometrics'.