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

Adam Richardson, Thomas van Florenstein Mulder, Tugrul Vehbi

Non-technical summary

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.