Nowcasting ‘true’ monthly U.S. GDP during the pandemic

Publication date: 23 Jun 2021 | Publication type: National Institute Economic Review | NIESR Author(s): Mitchell, J | External Author(s): Koop, G; McIntyre, S; Poon, A | JEL Classification: E01; E32 | Journal: National Institute Economic Review Issue 256 | Publisher: Cambridge University Press

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.

Keyword tags: 
pandemic
nowcasting
mixed-frequency vector autoregression
Bayesian