Benchmarking econometric and machine learning methodologies in nowcasting GDP

Daniel Hopp

Year of publication:	2024
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
Subject:	ARIMA models \| Bayesian methods \| Econometric models \| GDP \| Machine learning \| Macroeconomic forecasting \| Neural networks \| Vector autoregression models \| Künstliche Intelligenz \| Artificial intelligence \| Prognoseverfahren \| Forecasting model \| Neuronale Netze \| VAR-Modell \| VAR model \| Bruttoinlandsprodukt \| Gross domestic product \| Benchmarking \| Nationaleinkommen \| National income \| Ökonometrie \| Econometrics \| Ökonometrisches Modell \| Econometric model \| Wirtschaftsprognose \| Economic forecast \| ARMA-Modell \| ARMA model \| Schätztheorie \| Estimation theory \| Zeitreihenanalyse \| Time series analysis

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