Nowcasting GDP using machine learning methods
Published in Working Paper, 2022
This paper compares the ability of several econometric and machine learning methods to nowcast GDP in (pseudo) real-time. The analysis takes the example of Dutch GDP over the years 1992-2018 using abroad data set of monthly indicators. It discusses the forecast accuracy but also analyzes the use of information from the large data set of regressors. We find that the random forest forecast provides the most accurate nowcasts while using the different variables in a relative stable and equal manner.
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Recommended citation: Pick, A., Kant. D and J.M. de Winter (2022), Nowcasting GDP using machine learning methods, DNB Working Paper nr. 754