Deep Dynamic Factor Models

Paolo Andreini, Cosimo Izzo and Giovanni Ricco ()
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Paolo Andreini: Independent Researcher
Cosimo Izzo: Independent Researcher

No 2023-08, Working Papers from Center for Research in Economics and Statistics

Abstract: A novel deep neural network framework – that we refer to as Deep Dynamic Factor Model (D2FM) –, 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 D2FM improves over the performances of a state-of-the-art DFM.

Keywords: Machine Learning; Deep Learning; Autoencoders; Real-Time data; Time-Series; Forecasting; Nowcasting; Latent Component Models; Factor Models (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 C55 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2023-05-20
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Deep Dynamic Factor Models (2023)
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