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

Ana Galvão and Marta Lopresto

National Institute Economic Review, 2020, vol. 254, R1-R11

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.

Date: 2020
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Working Paper: Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth using a Mixed-Frequency Bottom-up Approach (2020)
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