Ilias Chronopoulos, Katerina Chrysikou, George Kapetanios, James Mitchell, Aristeidis Raftapostolos
arXiv 31 May 2023 · Econometrics · 6 citations (OpenAlex)
arXiv:2305.19921 · PDF · DOI · OpenAlex · Extracted main text
In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time series models. Containment or lockdown policies, as instigated at the national-level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black-box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time-variation in the effectiveness of specific containment policies.
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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 | Farrell, Max H., Tengyuan Liang, and Sanjog Misra (2021) Deep neural networks for estimation and inference | 1.000 | 15 | 4 | 100% |
| 2 | Hastie, Trevor, Andrea Montanari, Saharon Rosset, and Ryan J. Tibshi… (2022) Surprises in high-dimensional ridgeless least squares interpolation | 0.928 | 5 | 3 | 80% |
| 3 | Yarotsky, Dmitry (2017) Error bounds for approximations with deep relu networks | 0.811 | 4 | 2 | 100% |
| 4 | Kelly, Bryan T., Semyon Malamud, and Kangying Zhou (2022) The virtue of complexity everywhere | 0.737 | 3 | 3 | 67% |
| 5 | Bartlett, Peter L., Nick Harvey, Christopher Liaw, and Abbas Mehrabian (2019) Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks | 0.737 | 3 | 2 | 100% |
| 6 | Gu, Shihao, Bryan Kelly, and Dacheng Xiu (2021) Autoencoder asset pricing models | 0.737 | 3 | 2 | 100% |
| 7 | Yarotsky, Dmitry (2018) Optimal approximation of continuous functions by very deep relu networks | 0.737 | 3 | 2 | 100% |
| 8 | Gu, Shihao, Bryan Kelly, and Dacheng Xiu (2020) Empirical asset pricing via machine learning | 0.693 | 5 | 1 | 100% |
| 9 | Giacomini, Raffaella and Barbara Rossi (2010) Forecast comparisons in unstable environments | 0.644 | 3 | 2 | 67% |
| 10 | Chronopoulos, Ilias, Aristeidis Raftapostolos, and George Kapetanios (2023) Forecasting value-at-risk using deep neural network quantile regression self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 0cmFrom interpretability to inference: an estimation framework for universal approximators | 0.405 | 1 | 1 |
| 2 | NEW APPROXIMATION RESULTS AND OPTIMAL ESTIMATION FOR FULLY CONNECTED DEEP NEURAL NETWORKS | 0.405 | 1 | 1 |