Nowcasting world GDP growth with high‐frequency data

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5/1/2022, 6:28:19 PM
Factor Model
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Caroline Jardet and Baptiste Meunier Caroline Jardet: Centre de recherche de la Banque de France - Banque de France Baptiste Meunier: Centre de recherche de la Banque Centrale européenne - Banque Centrale Européenne AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique
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during crisis episodes when sudden swings in the economy make usual benchmark projections (IMFs or OECDs) quickly outdated.
big data ; high frequency ; large factor models ; mixed frequency ; nowcasting ; variable selection (search for similar items in EconPapers)
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Post-Print from HAL