Harnessing Machine Learning for Real-Time Inflation Nowcasting

Richard Schnorrenberger, Aishameriane Schmidt and Guilherme Valle Moura

Working Papers from DNB

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 (search for similar items in EconPapers)
JEL-codes: C53 C55 E31 E37 (search for similar items in EconPapers)
Date: 2024-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-mac and nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:dnb:dnbwpp:806

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