Comparing out-of-sample performance of machine learning methods to forecast U.S. GDP growth
Ba Chu, Shafiullah Qureshi
| Year of publication: |
2023
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|---|---|
| Authors: | Chu, Ba ; Qureshi, Shafiullah |
| Published in: |
Computational economics. - Dordrecht [u.a.] : Springer Science + Business Media B.V., ISSN 1572-9974, ZDB-ID 1477445-8. - Vol. 62.2023, 4, p. 1567-1609
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| Subject: | Artificial neural networks | Boosting algorithms | Dimensionality reduction methods | GDP growth | Lasso | MIDAS | Random forest | Ridge regression | Neuronale Netze | Neural networks | Prognoseverfahren | Forecasting model | Künstliche Intelligenz | Artificial intelligence | Wirtschaftswachstum | Economic growth | Nationaleinkommen | National income | USA | United States | Regressionsanalyse | Regression analysis | Algorithmus | Algorithm | Prognose | Forecast |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1007/s10614-022-10312-z [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014437505