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Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth
We run a ‘horse race’ among popular forecasting methods, including machine learning (ML) and deep learning (DL) methods, that are employed to forecast U.S. GDP growth. Given the unstable nature of GDP growth data, we implement a recursive forecasting strategy to calculate the out-of-sample performan...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9483293/ https://www.ncbi.nlm.nih.gov/pubmed/36157276 http://dx.doi.org/10.1007/s10614-022-10312-z |