Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany

Günter W. Beck, Kai Carstensen, Jan-Oliver Menz, Richard Schnorrenberger and Elisabeth Wieland

No 2930, Working Paper Series from European Central Bank

Abstract: We study how millions of granular and weekly household scanner data combined with machine learning can help to improve the real-time nowcast of German inflation. Our nowcasting exercise targets three hierarchy levels of inflation: individual products, product groups, and headline inflation. At the individual product level, we construct a large set of weekly scanner-based price indices that closely match their official counterparts, such as butter and coffee beans. Within a mixed-frequency setup, these indices significantly improve inflation nowcasts already after the first seven days of a month. For nowcasting product groups such as processed and unprocessed food, we apply shrinkage estimators to exploit the large set of scanner-based price indices, resulting in substantial predictive gains over autoregressive time series models. Finally, by adding high-frequency information on energy and travel services, we construct competitive nowcasting models for headline inflation that are on par with, or even outperform, survey-based inflation expectations. JEL Classification: E31, C55, E37, C53

Keywords: inflation nowcasting; machine learning methods; mixed-frequency modeling; scanner price data (search for similar items in EconPapers)
Date: 2024-04
New Economics Papers: this item is included in nep-big, nep-eec and nep-mon
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Working Paper: Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20242930

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