Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSorić, Petar-
dc.contributor.authorMonte Moreno, Enric-
dc.contributor.authorTorra Porras, Salvador-
dc.contributor.authorClavería González, Óscar-
dc.description.abstractThe 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
dc.format.extent24 p.-
dc.publisherUniversitat de Barcelona. Facultat d'Economia i Empresaca
dc.relation.isformatofReproducció del document publicat a:
dc.relation.ispartofIREA – Working Papers, 2022, IR22/10-
dc.relation.ispartofAQR – Working Papers, 2022, AQR22/07-
dc.relation.ispartofseries[WP E-IR22/10]ca
dc.relation.ispartofseries[WP E-AQR22/07]-
dc.rightscc-by-nc-nd, (c) Sorić et al., 2022-
dc.sourceDocuments de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))-
dc.subject.classificationAprenentatge automàtic-
dc.subject.classificationAnàlisi de sèries temporals-
dc.subject.classificationPrevisió econòmica-
dc.subject.classificationCorba de Phillipscat
dc.subject.otherMachine learning-
dc.subject.otherTime-series analysis-
dc.subject.otherEconomic forecasting-
dc.subject.otherPhillips curveeng
dc.titleDensity forecasts of inflation using Gaussian process regression modelsca
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))
AQR (Grup d’Anàlisi Quantitativa Regional) – Working Papers

Files in This Item:
File Description SizeFormat 
IR22_010_Soric+Monte+Torre+Claveria.pdf1.79 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons