Nowcasting World GDP Growth with High-Frequency Data

Jardet Caroline and Baptiste Meunier

Working papers from Banque de France

Abstract: The Covid-19 crisis has shown how high-frequency data can help tracking economic turning points in real-time. Our paper investigates whether high-frequency data can also improve the nowcasting performances for world GDP growth on quarterly or annual basis. To this end, we select a large dataset of 151 monthly and 39 weekly series for 17 advanced and emerging countries representing 68% of world GDP. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS) which allows us to take advantage of our large database and to combine different frequencies. Models that include weekly data significantly outperforms other models relying on monthly or quarterly indicators, both in- and out-of-sample. Breaking down our sample, we show that models with weekly data have similar nowcasting performances relative to other models during normal times but strongly outperform them during crisis episodes (2008-2009 and 2020). We finally construct a nowcasting model of annual world GDP growth incorporating weekly data which give timely (one every week) and accurate forecasts (close to IMF and OECD projections, but with a 1 to 3 months lead). Policy-wise, this model can provide an alternative benchmark projection for world GDP growth during crisis episodes when sudden swings in the economy make the usual benchmark projections (from the IMF or the OECD) rapidly outdated.

Keywords: Nowcasting; Mixed-Frequency Data; High-Frequency Data; World GDP; Large Factor Models (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2020
New Economics Papers: this item is included in nep-mac
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Nowcasting world GDP growth with high‐frequency data (2022)
Working Paper: Nowcasting world GDP growth with high‐frequency data (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:788

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