Short-Term CPI Inflation Forecasting: Probing with model combinations

Jorge Miguel Bravo, Najat El Mekkaoui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Forecasts of CPI inflation are critical in many public policy areas and private business planning. Many alternative approaches for selecting CPI forecasting models have been proposed. The standard practice to CPI forecasting is to pursue a winner-take-all perspective by which, for each dataset, a single believed to be the best model is selected from a set of competing approaches. However, model combination methods are becoming a common alternative to using a single time series method. We propose and apply a flexible Bayesian model averaging (BMA) approach of CPI inflation models to mitigate conceptual uncertainty and improve the short-term out-of-sample forecasting accuracy. The model space includes novel machine learning and deep learning algorithms and traditional univariate seasonal time series methods. The empirical results on the United States and Euro Area data reveal that BMA increases the predictive accuracy of CPI inflation forecasts in short-term exercises. The reduced out-of-sample forecast errors of BMA may be explained by their flexibility and capacity to select models that capture the diversity and complexity of inflation determinants and to estimate model weights that reflect the out-of-sample accuracy of a model.
Original languageEnglish
Title of host publicationInformation Systems and Technologies. WorldCIST 2022
EditorsAlvaro Rocha, Hojjat Adeli, Gintautas Dzemyda, Fernando Moreira
PublisherSpringer
Chapter56
Pages564-578
Number of pages15
Volume1
ISBN (Electronic)978-3-031-04826-5
ISBN (Print)978-3-031-04825-8
DOIs
Publication statusPublished - 11 May 2022
Event10th World Conference on Information Systems and Technologies (WorldCist'22) - Budva, Montenegro
Duration: 12 Apr 202214 Apr 2022
Conference number: 10th

Publication series

NameLecture Notes in Networks and Systems
PublisherSpringer
Volume468
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference10th World Conference on Information Systems and Technologies (WorldCist'22)
Abbreviated titleWorldCist'22
Country/TerritoryMontenegro
CityBudva
Period12/04/2214/04/22

Keywords

  • CPI inflation
  • Bayesian model averaging
  • Forecasting
  • Model confidence set
  • Machine learning
  • Deep learning
  • Time series methods

Fingerprint

Dive into the research topics of 'Short-Term CPI Inflation Forecasting: Probing with model combinations'. Together they form a unique fingerprint.

Cite this