Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297370 
Year of Publication: 
2024
Series/Report no.: 
ECB Working Paper No. 2930
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
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.
Subjects: 
Inflation nowcasting
machine learning methods
scanner price data
mixed-frequency modeling
JEL: 
E31
C55
E37
C53
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-6678-8
Document Type: 
Working Paper

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