Fiche du document
- ISIDORE Id: 10670/1.387bf7...
- emse-04624940
- ARXIV: 2011.07920
- doi: 10.1016/j.ijforecast.2022.04.009
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/arxiv/2011.07920
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijforecast.2022.04.009
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Inflation Forecasting Disaggregated Inflation Consumer Price Index Machine Learning Gated Recurrent Unit Recurrent Neural Networks C - Mathematical and Quantitative Methods/C.C4 - Econometric and Statistical Methods: Special Topics/C.C4.C45 - Neural Networks and Related Topics C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation Methods E - Macroeconomics and Monetary Economics/E.E3 - Prices, Business Fluctuations, and Cycles/E.E3.E31 - Price Level • Inflation • Deflation E - Macroeconomics and Monetary Economics/E.E3 - Prices, Business Fluctuations, and Cycles/E.E3.E37 - Forecasting and Simulation: Models and Applications -mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]Sujets proches
Prediction Forecasts FuturologyCiter ce document
Oren Barkan et al., « Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.1016/j.ijforecast.2022.04.009
Métriques
Partage / Export
Résumé
We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.
