Density forecasts of inflation using Gaussian process regression models

Petar Sorić, Enric Monte (), Salvador Torra () and Oscar Claveria
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Enric Monte: Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).
Salvador Torra: Riskcenter–IREA, University of Barcelona (UB).

No 202210, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

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.

Keywords: Machine learning; Gaussian process regression; Time-series analysis; Economic forecasting; Inflation; New Keynesian Phillips curve. JEL classification: C45; C51; C53; E31. (search for similar items in EconPapers)
Pages: 19 pages
Date: 2022-07, Revised 2022-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dem, nep-eec, nep-for and nep-mac
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Working Paper: Density forecasts of inflation using Gaussian process regression models (2022)
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