  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 these latent factors using
state-of-the-art time-varying parameter (TVP) regressions with shrinkage
priors. Using monthly real-time data for the US, our results suggest that
adding such non-linearities yields forecasts that are on average highly
competitive to ones obtained from methods using linear dimension reduction
techniques. Zooming into model performance over time moreover reveals that
controlling for non-linear relations in the data is of particular importance
during recessionary episodes of the business cycle.
