Forecasting inflation in a data-rich environment : the benefits of machine learning methods
Year of publication: |
2021
|
---|---|
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
|
Subject: | Big data | Inflation forecasting | LASSO | Machine learning | Random forest model | Inflationsrate | Inflation rate | Prognoseverfahren | Forecasting model | Künstliche Intelligenz | Artificial intelligence | USA | United States |
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