  A novel deep neural network framework -- that we refer to as Deep Dynamic
Factor Model (D$^2$FM) --, is able to encode the information available, from
hundreds of macroeconomic and financial time-series into a handful of
unobserved latent states. While similar in spirit to traditional dynamic factor
models (DFMs), differently from those, this new class of models allows for
nonlinearities between factors and observables due to the autoencoder neural
network structure. However, by design, the latent states of the model can still
be interpreted as in a standard factor model. Both in a fully real-time
out-of-sample nowcasting and forecasting exercise with US data and in a Monte
Carlo experiment, the D$^2$FM improves over the performances of a
state-of-the-art DFM.
