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.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
Seller: Mahler Books, PFLUGERVILLE, TX, U.S.A.
Paperback. Condition: Good. This book is in good condition; no remainder marks. It does have some cover shelfwear, edge wear, corner wear. Inside pages have no writing. ; Advanced Texts In Econometrics; 23.4 X 15.6 X 1.94 centimeters; 366 pages. Seller Inventory # 06SA24-878-269
Seller: MARCIAL PONS LIBRERO, MADRID, M, Spain
TAPA BLANDA. Condition: New. Seller Inventory # 100459486
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 80384-n
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9780198773139
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition. Seller Inventory # 80384
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9780198773139
Quantity: Over 20 available
Seller: Brook Bookstore On Demand, Napoli, NA, Italy
Condition: new. Questo è un articolo print on demand. Seller Inventory # 976f9ab2ead599c3f6be9dddb64ce3b8
Quantity: Over 20 available
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Condition: New. In. Seller Inventory # ria9780198773139_new
Quantity: Over 20 available
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 80384-n
Quantity: Over 20 available
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Paperback. Condition: New. 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. Seller Inventory # LU-9780198773139
Quantity: Over 20 available