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A review of forecasting techniques for large datasets

Published online by Cambridge University Press:  26 March 2020

Jana Eklund  and
George Kapetanios
Jana Eklund*
Affiliation:
Bank of England
George Kapetanios*
Affiliation:
Queen Mary, University of London
*
e-mail: [email protected]
e-mail: [email protected]
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Abstract

This paper aims to provide a brief and relatively non-technical overview of state-of-the-art forecasting with large data sets. We classify existing methods into four groups depending on whether data sets are used wholly or partly, whether a single model or multiple models are used and whether a small subset or the whole data set is being forecast. In particular, we provide brief descriptions of the methods and short recommendations where appropriate, without going into detailed discussions of their merits or demerits.

Keywords

Large datasetsfactorsforecast combinationsC01C53
Type
Articles
Information
National Institute Economic Review , Volume 203 , January 2008 , pp. 109 - 115
Copyright
Copyright © 2008 National Institute of Economic and Social Research

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Footnotes

The views expressed in this paper are those of the authors, and not necessarily those of the Bank of England.

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