Models developed for gross domestic product (GDP) growth forecasting tend to be extremely complex, relying on a large number of variables and parameters. Such complexity is not always to the benefit of the accuracy of the forecast. Economic complexity constitutes a framework that builds on methods developed for the study of complex systems to construct approaches that are less demanding than standard macroeconomic ones in terms of data requirements, but whose accuracy remains to be systematically benchmarked. Here we develop a forecasting scheme that is shown to outperform the accuracy of the five-year forecast issued by the International Monetary Fund (IMF) by more than 25% on the available data. The model is based on effectively representing economic growth as a two-dimensional dynamical system, defined by GDP per capita and ‘fitness’, a variable computed using only publicly available product-level export data. We show that forecasting errors produced by the method are generally predictable and are also uncorrelated to IMF errors, suggesting that our method is extracting information that is complementary to standard approaches. We believe that our findings are of a very general nature and we plan to extend our validations on larger datasets in future works.
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The authors wish to thank M. Cristelli for useful discussions and technical ideas about the methods proposed in this paper, as well as giving fundamental contributions to many of the foundations of the economic complexity framework. The authors wish to thank M. Cader (Lead of Country Analytics, IFC) for his support and application of these methods to IFC country strategy. The authors wish to thank K. Roster for feedback on the SPS methods from use in World Bank Group country strategy instruments. This work has been partly funded by the Italian PNR project ‘CRISIS-Lab’. The findings, interpretations and conclusions expressed herein are those of the authors and do not necessarily reflect the view of the World Bank Group, its Board of Directors or the governments they represent.
The authors declare no competing interests.
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Tacchella, A., Mazzilli, D. & Pietronero, L. A dynamical systems approach to gross domestic product forecasting. Nature Phys 14, 861–865 (2018). https://doi.org/10.1038/s41567-018-0204-y
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