Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/187600
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: http://www.ub.edu/irea/working_papers/2022/202210.pdf |
It is part of: | IREA – Working Papers, 2022, IR22/10 AQR – Working Papers, 2022, AQR22/07 |
URI: | http://hdl.handle.net/2445/187600 |
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 | Size | Format | |
---|---|---|---|---|
IR22_010_Soric+Monte+Torre+Claveria.pdf | 1.79 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License