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Nowcasting ‘True’ Monthly U.S. Gdp During The Pandemic

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  • Koop, Gary
  • McIntyre, Stuart
  • Mitchell, James
  • Poon, Aubrey
Abstract
Expenditure-side and income-side gross domestic product (GDP) are measured at the quarterly frequency and contain measurement error. Econometric methods exist for producing reconciled estimates of underlying true GDP from these noisy estimates. Recently, the authors of this paper developed a mixed-frequency reconciliation model which produces monthly estimates of true GDP. In the present paper, we investigate whether this model continues to work well in the face of the extreme observations that occurred during the pandemic year and consider several extensions of it. These include stochastic volatility and error distributions that are fat-tailed or explicitly allow for outliers.

Suggested Citation

  • Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2021. "Nowcasting ‘True’ Monthly U.S. Gdp During The Pandemic," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 44-70, April.
    • Handle: RePEc:cup:nierev:v:256:y:2021:i::p:44-70_4
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    Cited by:

    1. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    2. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.

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