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Forecasting UK inflation bottom up

Andreas Joseph (), Eleni Kalamara (), George Kapetanios () and Galina Potjagailo ()
Additional contact information
Andreas Joseph: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Eleni Kalamara: King’s College London
Galina Potjagailo: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH

No 915, Bank of England working papers from Bank of England

Abstract: We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series combined with a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods and non-linear machine learning models. We find that exploiting CPI item series over the period 2011–19 yields strong improvements in forecasting UK inflation against an autoregressive benchmark, above and beyond the gains from macroeconomic predictors. Ridge regression and other shrinkage methods perform best across specifications that include item-level data, yielding gains in relative forecast accuracy of up to 70% at the one-year horizon. Our results suggests that the combination of a large and relevant information set combined with efficient penalisation is key for good forecasting performance for this problem. We also provide a model-agnostic approach to address the general problem of model interpretability in high-dimensional settings based on model Shapley values, partial re-aggregation and statistical testing. This allows us to identify CPI divisions that consistently drive aggregate inflation forecasts across models and specifications, as well as to assess model differences going beyond forecast accuracy.

Keywords: Inflation; forecasting; machine learning; state space models; CPI disaggregated data; Shapley values (search for similar items in EconPapers)
JEL-codes: C32 C45 C53 C55 E37 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2021-03-26
New Economics Papers: this item is included in nep-big, nep-ets, nep-for, nep-mac, nep-mon and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0915

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