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"Density forecasts of inflation using Gaussian process regression models"

Author

Listed:
  • Petar Soric

    (Faculty of Economics & Business University of Zagreb.)

  • Enric Monte

    (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).)

  • Salvador Torra

    (Riskcenter–IREA, University of Barcelona (UB).)

  • Oscar Claveria

    (AQR–IREA, University of Barcelona (UB).)

Abstract

The present study uses Gaussian Process regression models for generating density forecasts of inflation within the New Keynesian Phillips curve (NKPC) framework. The NKPC is a structural model of inflation dynamics in which we include the output gap, inflation expectations, fuel world prices and money market interest rates as predictors. We estimate country-specific time series models for the 19 Euro Area (EA) countries. As opposed to other machine learning models, Gaussian Process regression allows estimating confidence intervals for the predictions. The performance of the proposed model is assessed in a one-step-ahead forecasting exercise. The results obtained point out the recent inflationary pressures and show the potential of Gaussian Process regression for forecasting purposes.

Suggested Citation

  • Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
    • Handle: RePEc:ira:wpaper:202210
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    References listed on IDEAS

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    More about this item

    Keywords

    Machine learning; Gaussian process regression; Time-series analysis; Economic forecasting; Inflation; New Keynesian Phillips curve. JEL classification: C45; C51; C53; E31.;
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