Deep Dynamic Factor Models

Paolo Andreini, Cosimo Izzo and Giovanni Ricco ()

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Abstract: 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.

Date: 2020-07, Revised 2023-05
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
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Citations: View citations in EconPapers (3)

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Working Paper: Deep Dynamic Factor Models (2023)
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