Sorić, PetarMonte Moreno, EnricTorra Porras, SalvadorClavería González, Óscar2022-07-122022-07-122022https://hdl.handle.net/2445/187600The 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.24 p.application/pdfengcc-by-nc-nd, (c) Sorić et al., 2022http://creativecommons.org/licenses/by-nc-nd/3.0/es/Aprenentatge automàticAnàlisi de sèries temporalsPrevisió econòmicaCorba de PhillipsMachine learningTime-series analysisEconomic forecastingPhillips curveinfo:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/openAccess