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

Adam Richardson, Thomas van Florenstein Mulder, Tugrul Vehbi

    Research output: Contribution to journalArticle


    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. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)941-948
    JournalInternational Journal of Forecasting
    Issue number2
    Publication statusPublished - 2021


    Dive into the research topics of 'Nowcasting GDP using machine-learning algorithms: A real-time assessment'. Together they form a unique fingerprint.

    Cite this