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REAL-TIME PROBABILISTIC NOWCASTS OF UK QUARTERLY GDP GROWTH USING A MIXED-FREQUENCY BOTTOM-UP APPROACH

Published online by Cambridge University Press:  03 November 2020

Ana Beatriz Galvão
Affiliation:
University of Warwick, e-mail: ana.galvao@wbs.ac.uk.
Marta Lopresto
Affiliation:
Bank of England.

Abstract

We propose a nowcasting system to obtain real-time predictive intervals for the first-release of UK quarterly GDP growth that can be implemented in a menu-driven econometric software. We design a bottom-up approach: forecasts for GDP components (from the output and the expenditure approaches) are inputs into the computation of probabilistic forecasts for GDP growth. For each GDP component considered, mixed-data-sampling regressions are applied to extract predictive content from monthly and quarterly indicators. We find that predictions from the nowcasting system are accurate, in particular when nowcasts are computed using monthly indicators 30 days before the GDP release. The system is also able to provide well-calibrated predictive intervals.

Type
Research Article
Copyright
© National Institute of Economic and Social Research, 2020

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Footnotes

This research has been funded by the ONS as part of the research programme of the Economic Statistics Centre of Excellence (ESCoE), and mainly carried out during the time that Marta Lopresto was working at the National Institute of Economic and Social Research (NIESR). We would like to thank Garry Young and Amit Kara for their suggestions and support. We are also grateful to referees from this Journal. Any views expressed are solely those of the author(s) and so cannot be taken to represent those of the Bank of England or to state Bank of England policy. This paper should therefore not be reported as representing the views of the Bank of England or members of the Monetary Policy Committee, Financial Policy Committee or Prudential Regulation Committee.

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