TY - JOUR
T1 - Forecasting UK inflation bottom up
AU - Joseph, Andreas
AU - Potjagailo, Galina
AU - Chakraborty, Chiranjit
AU - Kapetanios, George
N1 - Publisher Copyright:
© 2024 International Institute of Forecasters
PY - 2024/10/1
Y1 - 2024/10/1
N2 - 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.
AB - 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.
KW - CPI disaggregated data
KW - Forecasting
KW - Inflation
KW - Machine learning
KW - Shapley values
KW - State space models
UR - http://www.scopus.com/inward/record.url?scp=85183940393&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2024.01.001
DO - 10.1016/j.ijforecast.2024.01.001
M3 - Article
AN - SCOPUS:85183940393
SN - 0169-2070
VL - 40
SP - 1521
EP - 1538
JO - INTERNATIONAL JOURNAL OF FORECASTING
JF - INTERNATIONAL JOURNAL OF FORECASTING
IS - 4
ER -