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Title: Density forecasts of inflation using Gaussian process regression models
Author: Sorić, Petar
Monte Moreno, Enric
Torra Porras, Salvador
Clavería González, Óscar
Keywords: Aprenentatge automàtic
Anàlisi de sèries temporals
Previsió econòmica
Corba de Phillips
Machine learning
Time-series analysis
Economic forecasting
Phillips curve
Issue Date: 2022
Publisher: Universitat de Barcelona. Facultat d'Economia i Empresa
Series/Report no: [WP E-IR22/10]
[WP E-AQR22/07]
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.
Note: Reproducció del document publicat a:
It is part of: IREA – Working Papers, 2022, IR22/10
AQR – Working Papers, 2022, AQR22/07
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))
AQR (Grup d’Anàlisi Quantitativa Regional) – Working Papers

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