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

Bauwens, Luc, Lubrano, Michel, Richard, Jean-François

ISBN 10: 0198773137 / ISBN 13: 9780198773139

Edité par Oxford University Press, 2000

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N° de réf. du vendeur ERICA79701987731376

Détails bibliographiques

• Titre: Bayesian Inference in Dynamic Econometric Models (Advanced Texts in Econometrics)
• Éditeur: Oxford University Press
• Date de parution: 2000
• Langue: anglais
• ISBN à 10 chiffres: 0198773137
• ISBN à 13 chiffres: 9780198773139
• Reliure: Paperback
• État de l'article: Like New
• Type de livre: book

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Synopsis

This book offers an up-to-date coverage of the basic principles and tools of 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, and the long available analytical results of Bayesian inference for linear regression models.

À propos de l?auteur

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.