Nowcasting GDP using machine learning methods

Dennis Kant, Andreas Pick and Jasper de Winter ()

Working Papers from DNB

Abstract: 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 a broad 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.

Keywords: factor models; forecasting competition; machine learning methods; nowcasting. (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 (search for similar items in EconPapers)
Date: 2022-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-eec
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:dnb:dnbwpp:754

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