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Capturing GDP nowcast uncertainty in real time

Paul Labonne

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Abstract: This paper shows that modelling comovement in the asymmetry of the predictive distributions of GDP growth and a timely related series improves nowcasting uncertainty when it matters most : in times of severe economic downturn. Rather than using many predictors to nowcast GDP, I show that it is possible to extract more information than we currently do from series closely related to economic growth such as employment data. The proposed methodology relies on score driven techniques and provides an alternative approach for nowcasting besides dynamic factor models and MIDAS regression where dynamic asymmetry (or skewness) parameters have not yet been explored.

Date: 2020-12, Revised 2021-10
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
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Handle: RePEc:arx:papers:2012.02601