Giovanni Ballarin, Petros Dellaportas, Lyudmila Grigoryeva, Marcel Hirt, Sophie van Huellen, Juan-Pablo Ortega
arXiv 1 Nov 2022 · Econometrics · publishedInternational Journal of Forecasting (2023) · 23 citations (OpenAlex)
arXiv:2211.00363 · PDF · DOI · OpenAlex · Extracted main text
Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. MIxed-DAta Sampling (MIDAS) and Dynamic Factor Models (DFM) are the two main state-of-the-art approaches that allow modeling series with non-homogeneous frequencies. We introduce a new framework called the Multi-Frequency Echo State Network (MFESN) based on a relatively novel machine learning paradigm called reservoir computing. Echo State Networks (ESN) are recurrent neural networks formulated as nonlinear state-space systems with random state coefficients where only the observation map is subject to estimation. MFESNs are considerably more efficient than DFMs and allow for incorporating many series, as opposed to MIDAS models, which are prone to the curse of dimensionality. All methods are compared in extensive multistep forecasting exercises targeting US GDP growth. We find that our MFESN models achieve superior or comparable performance over MIDAS and DFMs at a much lower computational cost.
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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 | C. Jardet and B. Meunier (2022) Nowcasting world GDP growth with high-frequency data | 0.928 | 5 | 3 | 80% |
| 2 | M. Clements and A. Galvão (2008) Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States | 0.843 | 4 | 3 | 75% |
| 3 | J. H. Stock and M. W. Watson (2016) Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics | 0.843 | 4 | 3 | 75% |
| 4 | E. Andreou, E. Ghysels, and A. Kourtellos (2013) Should macroeconomic forecasters use daily financial data and how? | 0.811 | 4 | 2 | 100% |
| 5 | L. Gonon, L. Grigoryeva, and J.-P. Ortega (2023) Approximation error estimates for random neural networks and reservoir systems self | 0.811 | 4 | 2 | 100% |
| 6 | L. Grigoryeva and J.-P. Ortega (2018) Echo state networks are universal | 0.811 | 4 | 2 | 100% |
| 7 | A. Kostrov (2021) Essays on the use of MIDAS regressions in banking and finance | 0.737 | 4 | 3 | 50% |
| 8 | R. S. Mariano and Y. Murasawa (2003) A new coincident index of business cycles based on monthly and quarterly series | 0.737 | 4 | 3 | 50% |
| 9 | M. P. Clements and A. Galvão (2009) Forecasting US output growth using leading indicators: an appraisal using MIDAS models | 0.737 | 4 | 2 | 75% |
| 10 | E. Ghysels, P. Santa-Clara, and R. Valkanov (2004) The MIDAS touch: Mixed data sampling regression models | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 149 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles | 0.849 | 31 | 7 |