Ensemble Methods for Consumer Price Inflation Forecasting

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Abstract

Inflation forecasting is one of the central issues in micro and macroeconomics. Standard forecasting methods tend to follow a "winner-take-all" approach by which, for each time series, a single believed to be the best method is chosen from a pool of competing models. This paper investigates the predictive accuracy of a metalearning strategy called Arbitrated Dynamic Ensemble (ADE) in inflation forecasting using United States data. The findings show that: i) the SARIMA model exhibits the best average rank relative to ADE and competing state-of-theart model combination and metalearning methods; ii) the ADE methodology presents a better average rank compared to widely used model combination approaches, including the original Arbitrating approach, Stacking, Simple averaging, Fixed Share, or weighted adaptive combination of experts; iii) the ADE approach benefits from combining the base-learners as opposed to selecting the best forecasting model or using all experts; iv) the method is sensitive to the aggregation (weighting) mechanism.
Original languageEnglish
Title of host publicationCAPSI 2023 Proceedings
PublisherAPSI - Associação Portuguesa de Sistemas de Informação
Pages317-336
Number of pages20
DOIs
Publication statusPublished - 21 Oct 2023
Event23.ª Conferência da Associação Portuguesa de Sistemas de Informação - Beja, Portugal
Duration: 19 Oct 202321 Oct 2023
Conference number: 23
https://capsi2023.apsi.pt/index.php/pt/

Publication series

NameAtas da Conferência da Associação Portuguesa de Sistemas de Informação
PublisherAssociação Portuguesa de Sistemas de Informação
ISSN (Electronic)2183-489X

Conference

Conference23.ª Conferência da Associação Portuguesa de Sistemas de Informação
Abbreviated titleCAPSI 2023
Country/TerritoryPortugal
CityBeja
Period19/10/2321/10/23
Internet address

Keywords

  • Inflation
  • Time Series Forecasting
  • Model Combinations
  • Arbitrating
  • Stacking

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