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

Keywords

nowcastingGDP growthmixed frequency regressionforecast combinationprobabilistic forecastsC53E32
Type
Research Article
Information
National Institute Economic Review , Volume 254 , November 2020 , pp. R1 - R11
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.

References

REFERENCES

Anesti, N., Galvão, A. and Aggripino-Miranda, S. (2018), ‘Uncertain kingdom: nowcasting GDP and its revisions’, Bank of England Staff Working Paper Series no. 764.Google Scholar
Anesti, N., Hayes, S., Moreira, A. and Tasker, J. (2017), ‘Peering into the present: the Bank’s approach to GDP nowcasting’, Bank of England Quarterly Bulletin, Q2, pp. 122–33.Google Scholar
Bànbura, M., Giannone, D., Modugno, M. and Reichlin, L. (2013), ‘Now-casting and the real-time data flow’ in Handbook of Economic Forecasting, vol. 2A: Elsevier, pp. 195–237.Google Scholar
Bauwens, L., Laurent, S. and Rombouts, J.V.K. (2006), ‘Multivariate GARCH models: a survey’, Journal of Applied Econometrics, 21, pp. 79–109.CrossRefGoogle Scholar
Clark, T.E. (2012), ‘Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility’, Journal of Business and Economic Statistics, 29, pp. 327–41.CrossRefGoogle Scholar
Clements, M.P. (2017), ‘Assessing macro uncertainty in real-time when data are subject to revision’, Journal of Business and Economic Statistics, 35, pp. 420–33.CrossRefGoogle Scholar
Clements, M.P. and Galvão, A.B. (2008), ‘Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States’, Journal of Business and Economic Statistics, 26, pp. 546–54.CrossRefGoogle Scholar
Clements, M.P. and Galvão, (2013), ‘Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions’, Journal of Applied Econometrics, 28, pp. 458–77.CrossRefGoogle Scholar
Clements, M.P. and Galvão, , (2020), ‘Density forecasting with BVAR models under macroeconomic uncertainty’, WBS EMF Working Paper Series no. 36.Google Scholar
Foroni, C. and Marcellino, M. (2013), ‘A survey of econometrics methods for mixed-frequency data’, Norges Bank Working Paper no. 2013/06.Google Scholar
Foroni, C., Marcellino, M. and Schumacher, C. (2015) ‘U-MIDAS: MIDAS regressions with unrestricted lag polynomial’, Journal of the Royal Statistical Society, Series A, 178, pp. 57–82.CrossRefGoogle Scholar
Galvão, A. and Michell, M. (2019), ‘Measuring data uncertainty: an application using the Bank of England’s ‘Fan Charts’ for Historical GDP growth’, WBS EMF Working Paper Series no. 24.Google Scholar
Higgins, P. (2014), ‘GDPnow: a model GDP nowcasting’, Federal Reserve of Atlanta Working Paper Series, 2014–7.Google Scholar
Koop, G., McIntyre, S. and Mitchell, J. (2019), ‘UK regional nowcasting using a mixed frequency vector auto-regressive model with entropic tilting’, Journal of the Royal Statistical Society: Series A, 183, pp. 91–119.CrossRefGoogle Scholar
Mitchell, J. (2009), ‘Where are we now? The UK recession and nowcasting GDP growth using statistical models’, National Institute Economic Review, 209, pp. 60–69.CrossRefGoogle Scholar
Mitchell, J., Smith, R., Weale, M., Wright, S. and Salazar, E. (2005), ‘An indicator of monthly GDP and an early estimate of quarterly GDP growth’, The Economic Journal, 115, F108-F129.CrossRefGoogle Scholar
Pettenuzzo, D., Timmermann, A. and Valkanov, R. (2016), ‘A MIDAS approach to modeling first and second moment dynamics’, Journal of Econometrics, 193, pp. 315–34.CrossRefGoogle Scholar