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. It is found that exploiting CPI item series over the 2011–2019 period 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 1-year horizon. Our results suggest that the combination of a large and relevant information set combined with efficient penalisation is key to good forecasting performance in 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 forecasts across models and specifications, as well as to assess model differences going beyond accuracy.

我们使用大量每月分解的 CPI 项目系列以及一系列广泛的预​​测工具（包括降维技术、收缩方法和非线性机器学习模型）预测英国的 CPI 通胀率，最多可提前一年。研究发现，利用 2011-2019 年期间的 CPI 项目系列在预测英国通胀方面与自回归基准相比产生了显着改善，超出了宏观经济预测指标的收益。岭回归和其他收缩方法在包括项目级数据的规范中表现最佳，在 1 年范围内产生高达 70% 的相对预测准确度增益。我们的结果表明，将大量相关信息集与有效惩罚相结合是该问题良好预测性能的关键。我们还提供了一种与模型无关的方法，以解决基于模型 Shapley 值、部分重新聚合和统计测试的高维设置中模型可解释性的一般问题。这使我们能够识别 CPI 部门，持续推动跨模型和规格的预测，以及评估超出准确性的模型差异。