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Forecasting UK inflation bottom up
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AI
DOI：10.1016/j.ijforecast.2024.01.001.png)
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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. (c) 2024 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Inflation
Forecasting
Machine learning
State space models
CPI disaggregated data
Shapley values
期刊
IF:
7.1
论文数:
3.1K
被引数:
9.9K
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