Bayesian Inference in Dynamic Econometric Models

Bauwens, Luc; Lubrano, Michel; Richard, Jean-Francois

ISBN 10: 0198773129 / ISBN 13: 9780198773122

Editore: Oxford University Press, 2000

Riguardo questo articolo

Unread book in perfect condition. Codice articolo 80383

Dati bibliografici

Titolo: Bayesian Inference in Dynamic Econometric ...

Casa editrice: Oxford University Press

Data di pubblicazione: 2000

Legatura: Rilegato

Condizione: As New

Descrizione articolo

Riassunto:

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.

Recensione:

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)