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Nowcasting Peru's GDP with Machine Learning Methods

Author

Listed:
  • Jairo Flores

    (Central Reserve Bank of Peru)

  • Bruno Gonzaga

    (Central Reserve Bank of Peru)

  • Walter Ruelas-Huanca

    (Central Reserve Bank of Peru)

  • Juan Tang

    (Central Reserve Bank of Peru)

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. 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.

Suggested Citation

  • Jairo Flores & Bruno Gonzaga & Walter Ruelas-Huanca & Juan Tang, 2025. "Nowcasting Peru's GDP with Machine Learning Methods," IHEID Working Papers 01-2025, Economics Section, The Graduate Institute of International Studies.
  • Handle: RePEc:gii:giihei:heidwp01-2025
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    File URL: http://repec.graduateinstitute.ch/pdfs/Working_papers/HEIDWP01-2025.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    GDP; Machine Learning; nowcasting;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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