GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Klakow Akepanidtaworn and Korkrid Akepanidtaworn

No 2025/252, IMF Working Papers from International Monetary Fund

Abstract: Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in nowcasting across simulation and six country cases, traditional econometric models tend to outperform ML algorithms. Among the ML algorithms, linear ML algorithm – Lasso and Elastic Net – perform best in nowcasting, even surpassing traditional econometric models in cases of long GDP data and rich high-frequency indicators. Among the traditional econometric models, the Bridge and Dynamic Factor deliver the strongest empirical results, while Three-Pass Regression Filter performs well in our simulation. Due to the relatively short length of GDP series, complex and non-linear ML algorithms are prone to overfitting, which compromises their out-of-sample performance.

Keywords: Nowcasting; Machine Learning; Forecast evaluation; Real-time data; IMF working papers; machine-learning algorithm; GDP Nowcasting; high-frequency indicator; non-linear ML algorithm; Econometric models; Factor models; COVID-19 (search for similar items in EconPapers)
Pages: 48
Date: 2025-12-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets and nep-for
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