Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
arXiv 23 Dec 2020 · Econometrics · publishedJournal of Economic Surveys (2021) · 49 citations (OpenAlex)
arXiv:2012.12802 · PDF · DOI · OpenAlex · Extracted main text
In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.
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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 | Chen, X (2007) Large sample sieve estimation of semi-nonparametric models | 1.000 | 6 | 3 | 100% |
| 2 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection amongst high-dimensional controls | 0.874 | 6 | 2 | 100% |
| 3 | Medeiros, M. C., G. Vasconcelos, A. Veiga, and E. Zilberman (2021) Forecasting inflation in a data-rich environment: The benefits of machine learning methods self | 0.874 | 5 | 2 | 100% |
| 4 | Elliott, G., A. Gargano, and A. Timmermann (2013) Complete subset regressions | 0.843 | 3 | 3 | 100% |
| 5 | Garcia, M., M. Medeiros, and G. Vasconcelos (2017) Real-time inflation forecasting with high-dimensional models: The case of brazil | 0.737 | 3 | 2 | 100% |
| Medeiros | unmatched citation key Medeiros | 0.693 | 5 | 1 | 100% |
| Tibshirani | unmatched citation key Tibshirani | 0.693 | 5 | 1 | 100% |
| Hansen | unmatched citation key Hansen | 0.644 | 4 | 1 | 100% |
| 9 | van de Geer, S., P. Bühlmann, Y. Ritov, and R. Dezeure (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.644 | 4 | 1 | 100% |
| 10 | Babii, A., E. Ghysels, and J. Striaukas (2020) Machine learning time series regressions with an application to nowcasting | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 380 scored citations. 3 of these could not be matched to a bibliography entry, so only the citation key is shown.
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