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Nowcasting GDP using machine learning methods

Author

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  • Dennis Kant
  • Andreas Pick
  • Jasper de Winter

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 years 1992-2018 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 regressors. We find that the random forest forecast provides the most accurate nowcasts while using the different variables in a relative stable and equal manner.

Suggested Citation

  • Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    • Handle: RePEc:dnb:dnbwpp:754
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    References listed on IDEAS

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    Cited by:

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    2. Juan Tenorio & Wilder Perez, 2024. "Monthly GDP nowcasting with Machine Learning and Unstructured Data," Papers 2402.04165, arXiv.org.
    3. Kristian Jönsson, 2024. "Neighbor Weighting and Distance Metrics in Nearest Neighbor Nowcasting of Swedish GDP," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(4), pages 1077-1089, December.

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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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