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NOWCASTING ‘TRUE’ MONTHLY U.S. GDP DURING THE PANDEMIC

Published online by Cambridge University Press:  23 June 2021

Gary Koop Open the ORCID record for Gary Koop [Opens in a new window] ,
Stuart McIntyre ,
James Mitchell  and
Aubrey Poon
Gary Koop*
Affiliation:
Department of Economics, University of Strathclyde, Glasgow, United Kingdom Economic Statistics Centre of Excellence, London, United Kingdom
Stuart McIntyre
Affiliation:
Department of Economics, University of Strathclyde, Glasgow, United Kingdom Economic Statistics Centre of Excellence, London, United Kingdom
James Mitchell
Affiliation:
Economic Statistics Centre of Excellence, London, United Kingdom Federal Reserve Bank of Cleveland, Cleveland, Ohio, United States
Aubrey Poon
Affiliation:
Department of Economics, University of Strathclyde, Glasgow, United Kingdom Economic Statistics Centre of Excellence, London, United Kingdom
*
*Corresponding author. Email: gary.koop@strath.ac.uk
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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.

Keywords

pandemicnowcastingmixed-frequency vector autoregressionBayesianE01E32
Type
Research Article
Information
National Institute Economic Review , Volume 256: THE IMPACT OF COVID-19 ON MACROECONOMIC FORECASTING , Spring 2021 , pp. 44 - 70
Copyright
© National Institute Economic Review, 2021

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