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Sinopsis
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.
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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.
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Bayesian Inference in Dynamic Econometric Models
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Oxford University Press, Incorporated, 2000
ISBN 10: 0198773137
ISBN 13: 9780198773139
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Bayesian Inference in Dynamic Econometric Models (Advanced Texts in Econometrics)
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Bayesian Inference in Dynamic Econometric Models (Advanced Texts in Econometrics)
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OXFORD UNIVERSITY PRESS, Oxford, 1999
ISBN 10: 0198773137
ISBN 13: 9780198773139
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Bayesian Inference in Dynamic Econometric Models (Advanced Texts in Econometrics)
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ISBN 13: 9780198773139
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Bayesian Inference in Dynamic Econometric Models (Paperback)
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ISBN 13: 9780198773139
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Paperback. Condición: new. Paperback. 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 abroad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It containsalso an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods. This work contains an up-to-date coverage of the last 20 years' advances in Bayesian inference in econometrics, with an emphasis on dynamic models. Several examples illustrate the methods. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9780198773139
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