arXiv 26 Oct 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2310.17571 · PDF · DOI · OpenAlex · Extracted main text
Long short-term memory (LSTM) and gated recurrent unit (GRU) are used to model US recessions from 1967 to 2021. Their predictive performances are compared to those of the traditional linear models. The out-of-sample performance suggests the application of LSTM and GRU in recession forecasting, especially for longer-term forecasts. The Shapley additive explanations (SHAP) method is applied to both groups of models. The SHAP-based different weight assignments imply the capability of these types of neural networks to capture the business cycle asymmetries and nonlinearities. The SHAP method delivers key recession indicators, such as the S&P 500 index for short-term forecasting up to 3 months and the term spread for longer-term forecasting up to 12 months. These findings are robust against other interpretation methods, such as the local interpretable model-agnostic explanations (LIME) and the marginal effects.
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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 | Vrontos, S., Galakis, J., Vrontos, I (2021) Modeling and predicting u.s. recessions using machine learning techniques | 0.737 | 3 | 2 | 100% |
| 2 | Chauvet, M., Piger, J (2008) A comparison of the real-time performance of business cycle dating methods | 0.644 | 2 | 2 | 100% |
| 3 | Estrella, A., Mishkin, F (1996) The yield curve as a predictor of u.s. recessions, current issues in economics and finance | 0.644 | 2 | 2 | 100% |
| 4 | Estrella, A., Mishkin, F (1998) Predicting u.s. recessions: Financial variables as leading indicators | 0.644 | 2 | 2 | 100% |
| 5 | Hornik, K., Stinchcombe, M., White, H (1989) Multilayer feedforward networks are universal approximaters | 0.644 | 2 | 2 | 100% |
| 6 | Ng, S (2014) Viewpoint: boosting recessions | 0.644 | 2 | 2 | 100% |
| 7 | Puglia, M., Tucker, A (2021) Neural networks, the treasury yield curve, and recession forecasting | 0.585 | 3 | 1 | 100% |
| 8 | Bergstra, J., Bengio, Y (2012) Random search for hyper-parameter optimization | 0.511 | 2 | 1 | 100% |
| 9 | Lundberg, S., Lee, S (2017) A unified approach to interpreting model predictions | 0.511 | 2 | 1 | 100% |
| 10 | Molnar, C (2020) Interpretable machine learning: A guide for making black box models explainable 2ed | 0.511 | 2 | 1 | 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 | At-Risk Transformation for U.S. Recession Prediction $ $ | 0.405 | 1 | 1 |