Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth using a Mixed-Frequency Bottom-up Approach

Ana Beatriz Galvão () and Marta Lopresto ()
Authors registered in the RePEc Author Service: Ana Beatriz Galvão

Economic Statistics Centre of Excellence (ESCoE) Discussion Papers from Economic Statistics Centre of Excellence (ESCoE)

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: nowcasting; GDP growth; mixed frequency regression; forecast combination; probabilistic forecasts (search for similar items in EconPapers)
JEL-codes: C53 E32 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-for, nep-mac and nep-ore
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Journal Article: REAL-TIME PROBABILISTIC NOWCASTS OF UK QUARTERLY GDP GROWTH USING A MIXED-FREQUENCY BOTTOM-UP APPROACH (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:nsr:escoed:escoe-dp-2020-06

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