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When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage

Laurent Ferrara () and Anna Simoni

Working papers from Banque de France

Abstract: Nowcasting GDP growth is extremely useful for policy-makers to assess macroeconomic conditions in real-time. In this paper, we aim at nowcasting euro area GDP with a large database of Google search data. Our objective is to check whether this specific type of information can be useful to increase GDP nowcasting accuracy, and when, once we control for official variables. In this respect, we estimate shrunk bridge regressions that integrate Google data optimally screened through a targeting method, and we empirically show that this approach provides some gain in pseudo-real-time nowcasting of euro area GDP quarterly growth. Especially, we get that Google data bring useful information for GDP nowcasting for the four first weeks of the quarter when macroeconomic information is lacking. However, as soon as official data become available, their relative nowcasting power vanishes. In addition, a true real-time analysis confirms that Google data constitute a reliable alternative when official data are lacking.

Keywords: Nowcasting; Big data; Sure Independence Screening; Ridge Regularization. (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-eec and nep-mac
Date: 2019
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Working Paper: When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage (2019) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:717

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