High-frequency inflation forecasting: A two-step machine learning methodology

Osmar Bolivar Rosales ()

Latin American Journal of Central Banking (previously Monetaria), 2026, vol. 7, issue 1

Abstract: This study introduces a novel two-step machine learning methodology to generate high-frequency (daily and weekly) inflation forecasts in developing economies, where official statistics are typically available only at a monthly frequency and with delays. High-frequency forecasting here is interpreted as nowcasting or interpolation — real-time prediction ahead of official releases or within-period estimation using mixed-frequency indicators — while also serving as a data-augmentation strategy. In the first step, high-frequency predictors are aggregated to construct monthly-aligned features that serve as inputs for training machine learning models. In the second step, various feature selection techniques are evaluated and multiple machine learning algorithms are rigorously fine-tuned via hyperparameter optimization. Through systematic evaluation, a final model was selected — Ridge regression trained on an L1-regularized feature subset — that achieves superior out-of-sample accuracy. This model is then deployed to produce high-frequency year-on-year CPI inflation nowcasts. Forecasts exhibit strong temporal alignment with observed monthly values, while distributional equivalence — monthly vs. high-frequency projections — is confirmed via Kolmogorov–Smirnov tests. Compared to benchmark econometric models, the proposed approach delivers improved predictive performance, offering timely and granular insights for forward-looking monetary policy.

Keywords: Inflation; Forecasting; Nowcasting; Machine learning; High-frequency data; Mixed-frequency models; Data-augmentation (search for similar items in EconPapers)
JEL-codes: C22 C53 C55 E31 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:lajcba:v:7:y:2026:i:1:s2666143825000109

DOI: 10.1016/j.latcb.2025.100172

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