Essays in Econometrics

Name: Essays in Econometrics
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ISBN: 9780521796491

Collected Papers of Clive W. J. Granger

Clive W. J. Granger, Eric Ghysels (University of North Carolina, Chapel Hill), Norman R. Swanson (Rutgers University, New Jersey) , Mark W. Watson (Princeton University, New Jersey)

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English

Cambridge University Press
20 November 2001

Econometrics; Economic forecasting

Series: Econometric Society Monographs

This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

By: Clive W. J. Granger
Edited by: Eric Ghysels (University of North Carolina Chapel Hill), Norman R. Swanson (Rutgers University, New Jersey), Mark W. Watson (Princeton University, New Jersey)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Volume: Volume 2
Dimensions: Height: 231mm, Width: 136mm, Spine: 22mm
Weight: 530g
ISBN: 9780521796491
ISBN 10: 0521796490
Series: Econometric Society Monographs
Pages: 398
Publication Date: 20 November 2001
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Reviews for Essays in Econometrics: Collected Papers of Clive W. J. Granger

'Re-reading all these contributions is fascinating and eye-opening on the fundamental effect that Granger had on research in economics. ... this book constitutes a highly recommendable addition to your bookshelf.' De Economist The book is highly recommended as a reference for researchers on many important topics such as forecasting, non-linearity, causality, co-integration and long-memory. And it can also serve as a resource for applications of time series modeling to econometrics for practitioners. Mathematical Reviews