Forecasting inflation in a data-rich environment : the benefits of machine learning methods
Marcelo C. Medeiros, Gabriel F.R. Vasconcelos, Álvaro Veiga, Eduardo Zilberman
| Year of publication: |
2021
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|---|---|
| Authors: | Medeiros, Marcelo C. ; Vasconcelos, Gabriel F. R. ; Veiga, Alvaro ; Zilberman, Eduardo |
| Published in: |
Journal of business & economic statistics : JBES ; a publication of the American Statistical Association. - Abingdon : Taylor & Francis, ISSN 1537-2707, ZDB-ID 2043744-4. - Vol. 39.2021, 1, p. 98-119
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| Subject: | Big data | Inflation forecasting | LASSO | Machine learning | Random forest model | Inflationsrate | Inflation rate | Prognoseverfahren | Forecasting model | Künstliche Intelligenz | Artificial intelligence | USA | United States |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1080/07350015.2019.1637745 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012424502