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Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator

Author

Listed:
  • Luke Hartigan

    (The University of Sydney)

  • Tom Rosewall

    (Reserve Bank of Australia)

Abstract

What is happening now? The onset of the COVID-19 crisis highlighted the importance of having timely data on the economy to help policymakers make more informed decisions. However, the most comprehensive measure of activity, GDP, is published with a long lag, thereby limiting its value to policymakers as a measure of the current state of the economy. To overcome this information deficiency, we develop a monthly activity indicator (MAI) for Australia. The MAI aims to provide policymakers with a more immediate snapshot of prevailing economic conditions. We achieve this by using a dynamic factor model to summarise the information content from a curated list of 30 monthly predictors selected for their ability to explain movements in quarterly real GDP growth. We undertake a pseudo out-of-sample nowcasting exercise using the MAI in an unrestricted MIDAS model and find that nowcasts based on the MAI significantly outperform standard benchmarks. Crucially, outperformance is largest during the COVID-19 crisis, emphasising the benefit from considering monthly data. Our results demonstrate that the MAI is a useful tool for policymakers to gain a better understanding of current economic conditions in Australia.

Suggested Citation

  • Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," RBA Research Discussion Papers rdp2024-04, Reserve Bank of Australia.
    • Handle: RePEc:rba:rbardp:rdp2024-04
    DOI: 10.47688/rdp2024-04
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    More about this item

    Keywords

    COVID-19; dynamic factor model; forecast evaluation; GDP growth; MIDAS regression; nowcasting; real-time data;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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