Nowcasting Peruvian GDP with Machine Learning Methods

Jairo Flores Audante, Bruno Gonzaga, Walter Ruelas-Huanca and Juan Tang
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Walter Ruelas-Huanca: Banco Central de Reserva del Perú
Juan Tang: Banco Central de Reserva del Perú

No 2024-019, Working Papers from Banco Central de Reserva del Perú

Abstract: This paper explores the application of machine learning (ML) techniques to nowcast the monthly year-over-year growth rate of both total and non-primary GDP in Peru. Using a comprehensive dataset that includes over 170 domestic and international predictors, we assess the predictive performance of 12 ML models, including Lasso, Ridge, Elastic Net, Support Vector Regression, Random Forest, XGBoost, and Neural Networks. The study compares these ML approaches against the traditional Dynamic Factor Model (DFM), which serves as the benchmark for nowcasting in economic research. We treat specific configurations, such as the feature matrix rotations and the dimensionality reduction technique, as hyperparameters that are optimized iteratively by the Tree-Structured Parzen Estimator. Our results show that ML models outperformed DFM in nowcasting total GDP, and that they achieve similar performance to this benchmark in nowcasting non-primary GDP. Furthermore, the bottom-up approach appears to be the most effective practice for nowcasting economic activity, as aggregating sectoral predictions improves the precision of ML methods. The findings indicate that ML models offer a viable and competitive alternative to traditional nowcasting methods.

Keywords: GDP; Machine Learning; nowcasting (search for similar items in EconPapers)
JEL-codes: C14 C32 E32 E52 (search for similar items in EconPapers)
Date: 2024-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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