Nowcasting GDP using machine-learning algorithms: A real-time assessment

Can machine-learning algorithms help central banks understand the current state of the economy? Our results say yes! We contribute to the emerging literature on forecasting macroeconomic variables using machine-learning algorithms by testing the nowcast performance of common algorithms in a full ‘re...

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Veröffentlicht in:International journal of forecasting Jg. 37; H. 2; S. 941 - 948
Hauptverfasser: Richardson, Adam, van Florenstein Mulder, Thomas, Vehbi, Tuğrul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2021
Schlagworte:
Algorithms
Analysis
Central banks
Data mining
Forecast evaluation
Forecasting practice
Gross domestic product
Machine learning
Macroeconomic forecasting
Nowcasting
Forecast evaluation
Forecasting practice
Macroeconomic forecasting
Machine learning
Nowcasting
ISSN:0169-2070, 1872-8200
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Zusammenfassung:Can machine-learning algorithms help central banks understand the current state of the economy? Our results say yes! We contribute to the emerging literature on forecasting macroeconomic variables using machine-learning algorithms by testing the nowcast performance of common algorithms in a full ‘real-time’ setting—that is, with real-time vintages of New Zealand GDP growth (our target variable) and real-time vintages of around 600 predictors. Our results show that machine-learning algorithms are able to significantly improve over a simple autoregressive benchmark and a dynamic factor model. We also show that machine-learning algorithms have the potential to add value to, and in one case improve on, the official forecasts of the Reserve Bank of New Zealand.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2020.10.005