Welcome to the Granger Centre
The Granger Centre for Time Series Econometrics was established in December 2006 in the University of Nottingham's School of Economics to provide a research forum for the development and dissemination of new research initiatives in both theoretical and applied time series econometric analysis, including panel data methods.
The centre is named in honour of Professor Sir Clive Granger (1934-2009) in recognition both of his invaluable contributions to the discipline of time series econometrics and his long association with the University of Nottingham.
Sir Clive, who was awarded the Nobel Prize in Economic Sciences in 2003, had a profound influence in the field of time series analysis over almost half a century, becoming one of the most prominent econometricians in the world.
He died on May 27, 2009, at Scripps Memorial Hospital in San Diego, California. He leaves a legacy of research and analysis that will continue to be important for years to come.
Key aims and expertise
The primary roles of the Granger Centre are to:
- develop and encourage new research methods in time series specifically relevant to the detailed analysis of economic data
- place strong emphasis on the use of rigorous theoretical, applied and computational research methods to answer questions of interest to academic and professional economists alike
- facilitate rapid dissemination of new research in time series econometrics through a discussion paper programme, an annual themed conference, a workshop series and other occassional seminars
- have active External Fellowship and International Visitor schemes and to encourage collaboration between these researchers and Internal Fellows
- 22 November 2018 (16:00-17:00)
- C43 Sir Clive Granger
- Dynamic semiparametric factor model with structural breaks (C43, SCGB)
- 14 March 2019 (16:00-17:00)
- C43 Sir Clive Granger
- (C43, SCGB)
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Recent discussion papers
- Iliyan Georgiev, David I. Harvey, Stephen J. Leybourne and A. M. Robert Taylor
- August 2017
- David Harvey, Steve Leybourne and Emily Whitehouse show that in small, but empirically relevant, sample sizes, the long-run variance estimate used to compute the Diebold-Mariano test for forecast accuracy can frequently be negative. The authors consider a number of alternative approaches to estimating the long-run variance, and examine the finite sample performance of tests that use these differing approaches.
- June 2017
View all discussion papers