Nowcasting GDP using machine learning methods

Dennis Kant, Andreas Pick () and Jasper de Winter
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Andreas Pick: Erasmus University Rotterdam
Jasper de Winter: De Nederlandsche Bank

AStA Advances in Statistical Analysis, 2025, vol. 109, issue 1, No 1, 24 pages

Abstract: 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 period 1992Q1–2018Q4 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 macroeconomic and financial predictors. We find that, on average, the random forest provides the most accurate forecast and nowcasts, whilst the dynamic factor model provides the most accurate backcasts.

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: 2025
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DOI: 10.1007/s10182-024-00515-0

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