Harnessing Machine Learning for Real-Time Inflation Nowcasting

Richard Schnorrenberger*, Aishameriane Venes Schmidt, Guilherme Valle Moura

*Corresponding author for this work

Research output: Working paperAcademic

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.
Original languageEnglish
Volume806
DOIs
Publication statusPublished - 8 Mar 2024

Fingerprint

Dive into the research topics of 'Harnessing Machine Learning for Real-Time Inflation Nowcasting'. Together they form a unique fingerprint.

Cite this