  This paper applies neural network models to forecast inflation. The use of a
particular recurrent neural network, the long-short term memory model, or LSTM,
that summarizes macroeconomic information into common components is a major
contribution of the paper. Results from an exercise with US data indicate that
the estimated neural nets usually present better forecasting performance than
standard benchmarks, especially at long horizons. The LSTM in particular is
found to outperform the traditional feed-forward network at long horizons,
suggesting an advantage of the recurrent model in capturing the long-term trend
of inflation. This finding can be rationalized by the so called long memory of
the LSTM that incorporates relatively old information in the forecast as long
as accuracy is improved, while economizing in the number of estimated
parameters. Interestingly, the neural nets containing macroeconomic information
capture well the features of inflation during and after the Great Recession,
possibly indicating a role for nonlinearities and macro information in this
episode. The estimated common components used in the forecast seem able to
capture the business cycle dynamics, as well as information on prices.
