Philippe Goulet Coulombe, Maxime Leroux, Dalibor Stevanovic, Stéphane Surprenant
arXiv 4 Aug 2020 · Econometrics · publishedInternational Journal of Forecasting (2021) · 6 citations (OpenAlex)
arXiv:2008.01714 · PDF · DOI · OpenAlex · Extracted main text
In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions. However, when the forecasting technology either uses shrinkage or is nonlinear, it does. This is precisely the fabric of the machine learning (ML) macroeconomic forecasting environment. Pre-processing of the data translates to an alteration of the regularization -- explicit or implicit -- embedded in ML algorithms. We review old transformations and propose new ones, then empirically evaluate their merits in a substantial pseudo-out-sample exercise. It is found that traditional factors should almost always be included as predictors and moving average rotations of the data can provide important gains for various forecasting targets. Also, we note that while predicting directly the average growth rate is equivalent to averaging separate horizon forecasts when using OLS-based techniques, the latter can substantially improve on the former when regularization and/or nonparametric nonlinearities are involved.
appendix boundary found by appendix_command · 49% of the source is main text. Read the extracted text to check this.
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 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., and Surprenant, S (2019) How is machine learning useful for macroeconomic forecasting? self | 1.000 | 7 | 4 | 100% |
| 2 | Stock, J. H. and Watson, M. W (2002) Forecasting using principal components from a large number of predictors | 0.941 | 6 | 4 | 83% |
| 3 | Hastie, T., Tibshirani, R., and Friedman, J (2009) The elements of statistical learning: data mining, inference, and prediction | 0.928 | 5 | 3 | 80% |
| 4 | McCracken, M. W. and Ng, S (2016) FRED-MD: A monthly database for macroeconomic research | 0.928 | 4 | 3 | 100% |
| 5 | Shiller, R. J (1973) A distributed lag estimator derived from smoothness priors | 0.874 | 5 | 2 | 100% |
| 6 | Stock, J. H. and Watson, M. W (2002) Macroeconomic forecasting using diffusion indexes | 0.843 | 4 | 3 | 75% |
| 7 | Kim, H. H. and Swanson, N. R (2018) Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods | 0.843 | 3 | 3 | 100% |
| 8 | Medeiros, M. C., Vasconcelos, G. F., Veiga, A., and Zilberman, E (2019) Forecasting inflation in a data-rich environment: the benefits of machine learning methods | 0.843 | 3 | 3 | 100% |
| 9 | Banerjee, A., Marcellino, M., and Masten, I (2014) Forecasting with factor-augmented error correction models | 0.644 | 2 | 2 | 100% |
| 10 | Goulet Coulombe, P (2020) Time-varying parameters as ridge regressions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 64 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Can Machine Learning Catch the COVID-19 Recession? | 0.928 | 4 | 3 |
| 2 | 0cm Maximally Machine-Learnable Portfolios | 0.737 | 3 | 2 |
| 3 | The Macroeconomy as a Random Forest | 0.585 | 3 | 1 |
| 4 | 0.5cm dpd LGB+: A Macroeconomic Forecasting Road Test . 0.25cm | 0.511 | 2 | 1 |
| 5 | 0cm To Bag is to Prune | 0.405 | 1 | 1 |
| 6 | How is Machine Learning Useful for Macroeconomic Forecasting? | 0.405 | 1 | 1 |
| 7 | 0.54cm Time-Varying Parameters as Ridge Regressions | 0.405 | 1 | 1 |
| 8 | 0cm Slow-Growing Trees | 0.405 | 1 | 1 |
| 9 | 0.01cm darkpowderblue A Neural Phillips Curve and a Deep Output Gap | 0.405 | 1 | 1 |
| 10 | 1.4cm bred Maximally Forward-Looking Core Inflation | 0.405 | 1 | 1 |