Benchmarking econometric and machine learning methodologies in nowcasting GDP
Daniel Hopp
| Year of publication: |
2024
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|---|---|
| Authors: | Hopp, Daniel |
| Published in: |
Empirical economics : a quarterly journal of the Institute for Advanced Studies. - Berlin : Springer, ISSN 1435-8921, ZDB-ID 1462176-9. - Vol. 66.2024, 5, p. 2191-2247
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| Subject: | ARIMA models | Bayesian methods | Econometric models | GDP | Machine learning | Macroeconomic forecasting | Neural networks | Vector autoregression models | Wirtschaftsprognose | Economic forecast | Prognoseverfahren | Forecasting model | Neuronale Netze | Nationaleinkommen | National income | Künstliche Intelligenz | Artificial intelligence | VAR-Modell | VAR model | Zeitreihenanalyse | Time series analysis | Makroökonometrie | Macroeconometrics |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1007/s00181-023-02515-6 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014520141