Forecasting UK Inflation Bottom Up

Abstract

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series and a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods, and non-linear machine learning models. We find that over the full sample period 2002–21, the Ridge regression combined with CPI item series yields substantial improvement against an autoregressive benchmark at the six-month horizon, whereas the benchmark is hard to beat with other models and for other horizons. However, when considering periods of time where aggregate CPI inflation measures exhibit changes in momentum (rising or falling) or tail values, a wide range of models leads to substantial significant relative forecast gains. Exploiting CPI items through shrinkage methods yields strongest gains at horizons of 6–12 months when headline and core inflation measures are rising or falling. At shorter horizons and when inflation is rising, machine learning tools combined with CPI items and macroeconomic indicators are more useful. We also provide a model-agnostic approach based on model Shapley value decompositions to interpret and communicate signals from groups of items according to interpretable CPI categories.

Keywords: Inflation, forecasting, machine learning, state space models, CPI disaggregated data, Shapley values

JEL Classification: C32, C45, C53, C55, E37