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

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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 machine-learning algorithms are able to significantly improve over standard models used in economics to nowcast macroeconomic variables. We also show machine-learning algorithms have the potential to improve the official forecasts of the Reserve Bank of New Zealand.

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  • Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting GDP using machine learning algorithms: A real-time assessment," Reserve Bank of New Zealand Discussion Paper Series DP2019/03, Reserve Bank of New Zealand.
    • Handle: RePEc:nzb:nzbdps:2019/3
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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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