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Fan charts in era of big data and learning

Author

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  • Baruník, Jozef
  • Hanus, Luboš

Abstract

We propose how to construct big data-driven macroeconomic fan charts, using machine learning methods to reflect the information in 216 relevant economic variables. Such data-rich fan charts do not rely on restrictive model assumptions and allow the exploration of non-Gaussian, asymmetric, heavy-tailed data and their non-linear interactions. By allowing complex patterns to be learned from a data-rich environment, our fan charts are useful for decision making that depends on the uncertainty of a potentially large number of economic variables — most public policy issues.

Suggested Citation

  • Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:finlet:v:61:y:2024:i:c:s1544612324000333
    DOI: 10.1016/j.frl.2024.105003
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    More about this item

    Keywords

    Fan charts; Probabilistic forecasting; Machine learning; Deep learning; Macroeconomic time series;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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