arXiv 23 Jun 2020 · Econometrics · publishedJournal of Applied Econometrics (2024) · 42 citations (OpenAlex)
arXiv:2006.12724 · PDF · DOI · OpenAlex · Extracted main text
I develop Macroeconomic Random Forest (MRF), an algorithm adapting the canonical Machine Learning (ML) tool to flexibly model evolving parameters in a linear macro equation. Its main output, Generalized Time-Varying Parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, structural breaks/change) and allowing for sophisticated new ones. The approach delivers clear forecasting gains over numerous alternatives, predicts the 2008 drastic rise in unemployment, and performs well for inflation. Unlike most ML-based methods, MRF is directly interpretable -- via its GTVPs. For instance, the successful unemployment forecast is due to the influence of forward-looking variables (e.g., term spreads, housing starts) nearly doubling before every recession. Interestingly, the Phillips curve has indeed flattened, and its might is highly cyclical.
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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 | McCracken, M. and Ng, S (2020) Fred-qd: A quarterly database for macroeconomic research | 1.000 | 7 | 4 | 100% |
| 2 | Breiman, L (2001) Random forests | 1.000 | 6 | 4 | 100% |
| 3 | Cogley, T. and Sargent, T. J (2001) Evolving post-world war ii us inflation dynamics | 0.928 | 5 | 3 | 80% |
| 4 | Goulet Coulombe, P (2020) Time-varying parameters as ridge regressions | 0.920 | 9 | 5 | 78% |
| 5 | Friedberg, R., Tibshirani, J., Athey, S., and Wager, S (2018) Local linear forests | 0.874 | 7 | 2 | 100% |
| 6 | Blanchard, O., Cerutti, E., and Summers, L (2015) Inflation and activity–two explorations and their monetary policy implications | 0.874 | 6 | 2 | 100% |
| 7 | Del Negro, M., Lenza, M., Primiceri, G. E., and Tambalotti, A (2020) What’s up with the phillips curve? | 0.874 | 5 | 2 | 100% |
| 8 | Goulet Coulombe, P (2020) To bag is to prune | 0.843 | 4 | 3 | 75% |
| 9 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., Surprenant, S., et al (2019) How is machine learning useful for macroeconomic forecasting? | 0.843 | 5 | 3 | 60% |
| 10 | Kotchoni, R., Leroux, M., and Stevanovic, D (2019) Macroeconomic forecast accuracy in a data-rich environment | 0.843 | 3 | 3 | 100% |
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