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Nowcasting GDP using machine learning methods

Author

Listed:
  • Dennis Kant
  • Andreas Pick

    (Erasmus University Rotterdam)

  • Jasper de Winter

    (De Nederlandsche Bank)

Abstract

This paper compares the ability of several econometric and machine learning methods to nowcast GDP in (pseudo) real-time. The analysis takes the example of Dutch GDP over the period 1992Q1–2018Q4 using a broad data set of monthly indicators. It discusses the forecast accuracy but also analyzes the use of information from the large data set of macroeconomic and financial predictors. We find that, on average, the random forest provides the most accurate forecast and nowcasts, whilst the dynamic factor model provides the most accurate backcasts.

Suggested Citation

  • Dennis Kant & Andreas Pick & Jasper de Winter, 2025. "Nowcasting GDP using machine learning methods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(1), pages 1-24, March.
    • Handle: RePEc:spr:alstar:v:109:y:2025:i:1:d:10.1007_s10182-024-00515-0
    DOI: 10.1007/s10182-024-00515-0
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    More about this item

    Keywords

    Factor models; Forecasting competition; Machine learning methods; Nowcasting;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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