Forecasting UK inflation bottom up

Andreas Joseph*, Galina Potjagailo, Chiranjit Chakraborty, George Kapetanios

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We forecast CPI inflation indicators in the United Kingdom using a large set of monthly disaggregated CPI item series covering a sample period of twenty years, and employing a range of forecasting tools to deal with the high dimension of the set of predictors. Although an autoregressive model proofs hard to outperform overall, Ridge regression combined with CPI item series performs strongly in forecasting headline inflation. A range of shrinkage methods yields significant improvement over sub-periods where inflation was rising, falling or in the tails of its distribution. Once CPI item series are exploited, we find little additional forecast gain from including macroeconomic predictors. The forecast performance of non-parametric machine learning methods is relatively weak. Using Shapley values to decompose forecast signals exploited by a Random Forest, we show that the ability of non-parametric tools to flexibly switch between signals from groups of indicators may come at the cost of high variance and, as such, hurt forecast performance.

Original languageEnglish
Publication statusAccepted/In press - 2024


  • CPI disaggregated data
  • Forecasting
  • Inflation
  • Machine learning
  • Shapley values
  • State space models


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