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NOWCASTING ‘TRUE’ MONTHLY U.S. GDP DURING THE PANDEMIC
Department of Economics, University of Strathclyde, Glasgow, United Kingdom; Economic Statistics Centre of Excellence, London, United Kingdom.ORCID iD: 0000-0002-6091-378X
Department of Economics, University of Strathclyde, Glasgow, United Kingdom; Economic Statistics Centre of Excellence, London, United Kingdom.
Economic Statistics Centre of Excellence, London, United Kingdom; Federal Reserve Bank of Cleveland, Cleveland Ohio, United States.
Department of Economics, University of Strathclyde, Glasgow, United Kingdom; Economic Statistics Centre of Excellence, London, United Kingdom.ORCID iD: 0000-0003-2587-8779
2021 (English)In: National Institute Economic Review, ISSN 0027-9501, E-ISSN 1741-3036, Vol. 256, p. 44-70Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Cambridge University Press, 2021. Vol. 256, p. 44-70
Keywords [en]
pandemic, nowcasting, mixed-frequency vector autoregression, Bayesian
National Category
Economics
Identifiers
URN: urn:nbn:se:oru:diva-96369DOI: 10.1017/nie.2021.8ISI: 000665018700005Scopus ID: 2-s2.0-85126730640OAI: oai:DiVA.org:oru-96369DiVA, id: diva2:1626318
Available from: 2022-01-11 Created: 2022-01-11 Last updated: 2023-12-08Bibliographically approved

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Poon, Aubrey

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