Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282982 
Year of Publication: 
2023
Series/Report no.: 
Deutsche Bundesbank Discussion Paper No. 34/2023
Publisher: 
Deutsche Bundesbank, Frankfurt a. M.
Abstract: 
We study how millions of highly granular and weekly household scanner data combined with novel machine learning techniques can help to improve the nowcast of monthly German inflation in real time. Our nowcasting exercise targets three hierarchy levels of the official consumer price index. First, we construct a large set of weekly scanner-based price indices at the lowest aggregation level underlying official German inflation, such as those of butter and coffee beans. We show that these indices track their official counterparts extremely well. Within a mixed-frequency modeling framework, we also demonstrate that these scanner-based price indices improve inflation nowcasts at this very narrow level, notably already after the first seven days of a month. Second, we apply shrinkage estimators to exploit the large set of scanner-based price indices in nowcasting product groups such as processed and unprocessed food. This yields substantial predictive gains compared to a time series benchmark model. Finally, we nowcast headline inflation. Adding high-frequency information on energy and travel services, we construct highly competitive nowcasting models that are on par with, or even outperform, survey-based inflation expectations that are notoriously difficult to beat.
Subjects: 
Inflationnowcasting
machine learningmethods
scannerprice data
mixed-frequency modeling
JEL: 
E31
C55
E37
C53
Persistent Identifier of the first edition: 
ISBN: 
978-3-95729-969-7
Document Type: 
Working Paper

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