Nonlinear inflation forecasting with recurrent neural networks

Motivated by the recent literature that finds that artificial neural networks (NN) can efficiently predict economic time-series in general and inflation in particular, we investigate if the forecasting performance can be improved even further by using a particular kind of NN-a recurrent neural network. We use a long short-term memory recurrent neural network (LSTM) that was proven to be highly efficient for sequential data and computed univariate forecasts of monthly US CPI inflation. We show that even though LSTM slightly outperforms autoregressive model (AR), NN, and Markov-switching models, its performance is on par with the seasonal autoregressive model SARIMA. Additionally, we conduct a sensitivity analysis with respect to hyperparameters and provide a qualitative interpretation of what the networks learn by applying a novel layer-wise relevance propagation technique.