Nowcasting world GDP growth with high‐frequency data

Caroline Jardet and Baptiste Meunier
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Caroline Jardet: Centre de recherche de la Banque de France - Banque de France

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Abstract: Although the Covid-19 crisis has shown how high-frequency data can help track the economy in real time, we investigate whether it can improve the nowcasting accuracy of world GDP growth. To this end, we build a large dataset of 718 monthly and 255 weekly series. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS), which we extend with a preselection of variables. We find that this preselection markedly enhances performances. This approach also outperforms a LASSO-MIDAS—another technique for dimension reduction in a mixed-frequency setting. Though we find that a FA-MIDAS with weekly data outperform other models relying on monthly or quarterly data, we also point to asymmetries. Models with weekly data have indeed performances similar to other models during "normal" times but can strongly outperform them during "crisis" episodes, above all the Covid-19 period. Finally, we build a nowcasting model for world GDP annual growth incorporating weekly data that give timely (one per week) and accurate forecasts (close to IMF and OECD projections but with 1- to 3-month lead). Policy-wise, this can provide an alternative benchmark for world GDP growth during crisis episodes when sudden swings in the economy make usual benchmark projections (IMF's or OECD's) quickly outdated.

Keywords: big data; high frequency; large factor models; mixed frequency; nowcasting; variable selection (search for similar items in EconPapers)
Date: 2022-09
New Economics Papers: this item is included in nep-big, nep-for and nep-mac
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-03647097v1
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Citations: View citations in EconPapers (6)

Published in Journal of Forecasting, 2022, 41 (6), pp.1181-1200. ⟨10.1002/for.2858⟩

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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 (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03647097

DOI: 10.1002/for.2858

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