  In this paper, we assess whether using non-linear dimension reduction
techniques pays off for forecasting inflation in real-time. Several recent
methods from the machine learning literature are adopted to map a large
dimensional dataset into a lower dimensional set of latent factors. We model
the relationship between inflation and the latent factors using constant and
time-varying parameter (TVP) regressions with shrinkage priors. Our models are
then used to forecast monthly US inflation in real-time. The results suggest
that sophisticated dimension reduction methods yield inflation forecasts that
are highly competitive to linear approaches based on principal components.
Among the techniques considered, the Autoencoder and squared principal
components yield factors that have high predictive power for one-month- and
one-quarter-ahead inflation. Zooming into model performance over time reveals
that controlling for non-linear relations in the data is of particular
importance during recessionary episodes of the business cycle or the current
COVID-19 pandemic.
