Paolo Andreini, Cosimo Izzo, Giovanni Ricco
arXiv 23 Jul 2020 · Econometrics · 2 citations (OpenAlex)
arXiv:2007.11887 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Banbura and Modugno (2014) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data | 1.000 | 9 | 5 | 100% |
| 2 | Doz, Giannone and Reichlin (2012) A quasi–maximum likelihood approach for large, approximate dynamic factor models | 1.000 | 5 | 3 | 100% |
| 3 | Banbura, Giannone and Reichlin (2010) Nowcasting | 0.811 | 4 | 2 | 100% |
| 4 | Giannone, Reichlin and Small (2008) Nowcasting: The real-time informational content of macroeconomic data | 0.811 | 4 | 2 | 100% |
| 5 | Bai and Ng (2008) Forecasting economic time series using targeted predictors | 0.737 | 3 | 2 | 100% |
| 6 | Goodfellow, Bengio and Courville (2016) Deep Learning | 0.737 | 3 | 2 | 100% |
| 7 | Gu, Kelly and Xiu (2019) Autoencoder asset pricing models | 0.737 | 3 | 2 | 100% |
| 8 | McCracken and Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.737 | 3 | 2 | 100% |
| 9 | Stock and Watson (2002) Forecasting using principal components from a large number of predictors | 0.737 | 3 | 2 | 100% |
| 10 | Kingma and Ba (2014) Adam: A Method for Stochastic Optimization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 79 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 1.4cm bred From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks | 0.405 | 1 | 1 |