Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks
提出一种层次递归神经网络模型，利用CPI层级结构中的高层信息改进低层成分预测，在美国CPI-U数据上显著优于多种基准方法，为政策制定者和市场参与者提供更精细的物价变化预测工具。
We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.