Harnessing Machine Learning for Real-Time Inflation Nowcasting

43 Pages Posted: 9 Mar 2024

See all articles by Richard Schnorrenberger

Richard Schnorrenberger

University of Kiel - Institute of Statistics and Econometrics

Aishameriane Schmidt

De Nederlandsche Bank - Research Department; Erasmus University Rotterdam (EUR) - Department of Econometrics; Tinbergen Institute

Guilherme valle Moura

University of Kiel - Faculty of Economics and Social Sciences

Date Written: March 8, 2024

Abstract

We investigate the predictive ability of machine learning methods to produce weekly inflation nowcasts using high-frequency macro-financial indicators and a survey of professional forecasters. Within an unrestricted mixed-frequency ML framework, we provide clear guidelines to improve inflation nowcasts upon forecasts made by specialists. First, we find that variable selection performed via the LASSO is fundamental for crafting an effective ML model for inflation nowcasting. Second, we underscore the relevance of timely data on price indicators and SPF expectations to better discipline our model-based nowcasts, especially during the inflationary surge following the COVID-19 crisis. Third, we show that predictive accuracy substantially increases when the model specification is free of ragged edges and guided by the real-time data release of price indicators. Finally, incorporating the most recent high-frequency signal is already sufficient for real-time updates of the nowcast, eliminating the need to account for lagged high-frequency information.

Keywords: inflation nowcasting, machine learning, mixed-frequency data, survey of professional forecasters

JEL Classification: E31, E37, C53, C55

Suggested Citation

Schnorrenberger, Richard and Venes Schmidt, Aishameriane and Moura, Guilherme valle, Harnessing Machine Learning for Real-Time Inflation Nowcasting (March 8, 2024). De Nederlandsche Bank Working Paper No. 806, Available at SSRN: https://ssrn.com/abstract=4752626 or http://dx.doi.org/10.2139/ssrn.4752626

Richard Schnorrenberger (Contact Author)

University of Kiel - Institute of Statistics and Econometrics ( email )

Olshausensrabe 40-60
D-24118 Kiel
Germany

Aishameriane Venes Schmidt

De Nederlandsche Bank - Research Department ( email )

P.O. Box 98
1000 AB Amsterdam
Netherlands

Erasmus University Rotterdam (EUR) - Department of Econometrics ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

Guilherme valle Moura

University of Kiel - Faculty of Economics and Social Sciences ( email )

Westring 425
D-24118 Kiel, 24161
Germany

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