Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks

Oren Barkan, Jonathan Benchimol, Itamar Caspi, Allon Hammer, Noam Koenigstein

Research output: Working paper / PreprintDiscussion paper


We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) 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 prices.
Translated title of the contributionחיזוי רכיבי אינפלציה של מדד המחירים לצרכן עם רשת עצבית חוזרת ונשנית היררכית
Original languageEnglish
Place of PublicationJerusalem, Israel
PublisherBank of Israel
Number of pages30
StatePublished - Mar 2021


  • Inflation forecasting
  • Disaggregated inflation
  • Consumer Price Index
  • Machine learning
  • Gated Recurr

ULI Keywords

  • uli
  • Consumer price indexes
  • Inflation (Finance)
  • Neural networks (Computer science)
  • Consumer price index
  • Cost of living indexes
  • CPI
  • Retail price indexes
  • Artificial neural networks
  • Nets, Neural (Computer science)
  • Networks, Neural (Computer science)
  • Neural nets (Computer science)


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