  This paper applies a recurrent neural network, the LSTM, to forecast
inflation. This is an appealing model for time series as it processes each time
step sequentially and explicitly learns dynamic dependencies. The paper also
explores the dimension reduction capability of the model to uncover
economically-meaningful factors that can explain the inflation process. Results
from an exercise with US data indicate that the estimated neural nets present
competitive, but not outstanding, performance against common benchmarks
(including other machine learning models). The LSTM in particular is found to
perform well at long horizons and during periods of heightened macroeconomic
uncertainty. Interestingly, LSTM-implied factors present high correlation with
business cycle indicators, informing on the usefulness of such signals as
inflation predictors. The paper also sheds light on the impact of network
initialization and architecture on forecast performance.
