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Economic Forecasting in An Epidemic: A Break from the Past?

Author

Listed:
  • Chow, Hwee Kwan

    (Singapore Management University)

  • Choy, Keen Meng

Abstract

This paper aims to investigate whether the predictive ability and behaviour of professional forecasters are different during the Covid-19 epidemic as compared with the global financial crisis of 2008 and normal times. To this end, we utilise a survey of professional forecasters in Singapore collated by the central bank to analyse the forecasting record for GDP growth and CPI inflation. We first examine the point forecasts to document the extent of forecast failure in the pandemic crisis and test for behavioural explanations of the possible sources of forecast errors such as leader following and herding behaviour. Using percentile-based summary measures of probability distribution forecasts, we then study how the degree of consensus and subjective uncertainty among forecasters are affected by the heightened economic uncertainty during crises. We found the behaviour of forecasters do not differ much between the two crisis episodes for growth projections despite major differences between the two crises. As for inflation forecasts, our findings suggest forecasters suffer less from forecast inertia when predicting short term one-quarter ahead inflation as compared to longer term one-year and two-year ahead inflation.

Suggested Citation

  • Chow, Hwee Kwan & Choy, Keen Meng, 2022. "Economic Forecasting in An Epidemic: A Break from the Past?," Economics and Statistics Working Papers 10-2021, Singapore Management University, School of Economics.
  • Handle: RePEc:ris:smuesw:2021_010
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    File URL: https://ink.library.smu.edu.sg/soe_research/2549/
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    More about this item

    Keywords

    Survey data; COVID-19; leader following and herding behaviour; disagreement; uncertainty;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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