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

Adam Richardson, Thomas van Florenstein Mulder and Tugrul Vehbi ()

International Journal of Forecasting, 2021, vol. 37, issue 2, 941-948

Abstract: 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.

Keywords: Nowcasting; Machine learning; Forecast evaluation; Forecasting practice; Macroeconomic forecasting (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (23)

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Related works:
Chapter: Nowcasting New Zealand GDP using machine learning algorithms (2019)
Working Paper: Nowcasting GDP using machine learning algorithms: A real-time assessment (2019)
Working Paper: Nowcasting New Zealand GDP using machine learning algorithms (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:2:p:941-948

DOI: 10.1016/j.ijforecast.2020.10.005

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