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An Interpretable Machine Learning Workflow with an Application to Economic Forecasting

Author

Listed:
  • Marcus Buckmann

    (Bank of England)

  • Andreas Joseph

    (Bank of England)

Abstract

When the future state of the economy is uncertain, yet a central bank has more information about the possible scenarios, how should the central bank communicate its private information to the public? This paper analyzes the optimal tone of central bank Delphic forward guidance using the Bayesian persuasion model (Kamenica and Gentzkow 2011). Assuming that monetary policy is an exogenously given function over the states of the economy and that the central bank is precommitted to a forward-guidance policy function, under certain conditions, the optimal tone of communication about the uncertain future is overly pessimistic.

Suggested Citation

  • Marcus Buckmann & Andreas Joseph, 2023. "An Interpretable Machine Learning Workflow with an Application to Economic Forecasting," International Journal of Central Banking, International Journal of Central Banking, vol. 19(4), pages 449-522, October.
    • Handle: RePEc:ijc:ijcjou:y:2023:q:4:a:10
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    References listed on IDEAS

    as
    1. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
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    Cited by:

    1. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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