Nowcasting New Zealand GDP using machine learning algorithms

Adam Richardson, Thomas van Florenstein Mulder and Tugrul Vehbi ()

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: This paper analyses the real-time nowcasting performance of machine learning algorithms estimated on New Zealand data. Using a large set of real-time quarterly macroeconomic indicators, we train a range of popular machine learning algorithms and nowcast real GDP growth for each quarter over the 2009Q1-2018Q1 period. We compare the predictive accuracy of these nowcasts with that of other traditional univariate and multivariate statistical models. We find that the machine learning algorithms outperform the traditional statistical models. Moreover, combining the individual machine learning nowcasts further improves the performance than in the case of the individual nowcasts alone.

Keywords: Nowcasting; Machine learning; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C52 C53 C55 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2018-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Citations: View citations in EconPapers (23)

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https://cama.crawford.anu.edu.au/sites/default/fil ... son_mulder_vehbi.pdf (application/pdf)

Related works:
Journal Article: Nowcasting GDP using machine-learning algorithms: A real-time assessment (2021)
Chapter: Nowcasting New Zealand GDP using machine learning algorithms (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2018-47

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