A dynamical systems approach to gross domestic product forecasting

Abstract

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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Fig. 1: Graphic scheme of the SPS method and its combination with the past growth.
Fig. 2: Expected errors’ properties of the forecasting.
Fig. 3: Dynamics and forecasts in the GDP–fitness plane.

References

  1. 1.

    Cristelli, M. C. A., Tacchella, A., Cader, M. Z., Roster, K. I. & Pietronero, L. On the predictability of growth Policy Research Working Paper WPS 8117 (The World Bank, 2017).

  2. 2.

    Long-Term Projections of Asian GDP and Trade (Asian Development Bank, 2011).

  3. 3.

    World Economic Outlook. Subdued Demand: Symptoms and Remedies (IMF, 2016).

  4. 4.

    Cecconi, F., Cencini, M., Falcioni, M. & Vulpiani, A. Predicting the future from the past: An old problem from a modern perspective. Am. J. Phys. 80, 1001–1008 (2012).

    ADS  Article  Google Scholar 

  5. 5.

    Little, D. Varieties of Social Explanation (Westview, Boulder, CO, 1991).

  6. 6.

    Lorenz, E. N. Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci. 26, 636–646 (1969).

    ADS  Article  Google Scholar 

  7. 7.

    Ye, H. et al. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proc. Natl Acad. Sci. USA 112, E1569–E1576 (2015).

    Article  Google Scholar 

  8. 8.

    Calude, C. S. & Longo, G. The deluge of spurious correlations in big data. Found. Sci. 22, 595–612 (2017).

    MathSciNet  Article  Google Scholar 

  9. 9.

    Hausmann, R. & Hidalgo, C. A. The network structure of economic output. J. Econ. Growth 16, 309–342 (2011).

    Article  Google Scholar 

  10. 10.

    Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A. & Pietronero, L. A new metrics for countries’ fitness and products’ complexity. Sci. Rep. 2, 723 (2012).

    ADS  Article  MATH  Google Scholar 

  11. 11.

    Di Clemente, R., Chiarotti, G. L., Cristelli, M., Tacchella, A. & Pietronero, L. Diversification versus specialization in complex ecosystems. PLoS One 9, e112525 (2014).

    ADS  Article  Google Scholar 

  12. 12.

    Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A. & Pietronero, L. Economic complexity: conceptual grounding of a new metrics for global competitiveness. J. Econ. Dyn. Control 37, 1683–1691 (2013).

    MathSciNet  Article  MATH  Google Scholar 

  13. 13.

    Zaccaria, A., Cristelli, M., Tacchella, A. & Pietronero, L. How the taxonomy of products drives the economic development of countries. PLoS One 9, e113770 (2014).

    ADS  Article  Google Scholar 

  14. 14.

    Cristelli, M., Gabrielli, A., Tacchella, A., Caldarelli, G. & Pietronero, L. Measuring the intangibles: A metrics for the economic complexity of countries and products. PLoS One 8, e70726 (2013).

    ADS  Article  MATH  Google Scholar 

  15. 15.

    Cristelli, M., Tacchella, A. & Pietronero, L. The heterogeneous dynamics of economic complexity. PLoS One 10, e0117174 (2015).

    Article  Google Scholar 

  16. 16.

    Balassa, B. Trade liberalisation and “revealed” comparative advantage. Manch. Sch. 33, 99–123 (1965).

    Article  Google Scholar 

  17. 17.

    Pritchett, L. & Summers, L. H. Asiaphoria Meets Regression to the Mean NBER Working Paper 20573 (National Bureau of Economic Research, 2014).

  18. 18.

    Dreher, A., Marchesi, S. & Vreeland, J. R. The political economy of IMF forecasts. Public Choice 137, 145–171 (2008).

    Article  Google Scholar 

  19. 19.

    Batchelor, R. How useful are the forecasts of intergovernmental agencies? the IMF and OECD versus the consensus. Appl. Econ. 33, 225–235 (2001).

    Article  Google Scholar 

  20. 20.

    Frenkel, M., Rülke, J.-C. & Zimmermann, L. Do private sector forecasters chase after IMF or OECD forecasts? J. Macroecon. 37, 217–229 (2013).

    Article  Google Scholar 

  21. 21.

    Pugliese, E., Chiarotti, G. L., Zaccaria, A. & Pietronero, L. Complex economies have a lateral escape from the poverty trap. PLoS One 12, e0168540 (2017).

    Article  Google Scholar 

  22. 22.

    Solow, R. M. A contribution to the theory of economic growth. Q. J. Econ. 70, 65–94 (1956).

    Article  Google Scholar 

  23. 23.

    Pugliese, E., Napolitano, L., Zaccaria, A. & Pietronero, L. Coherent diversification in corporate technological portfolios. Preprint at https://arXiv.org/abs/1707.02188 (2017).

  24. 24.

    Battiston, F., Cristelli, M., Tacchella, A. & Pietronero, L. How metrics for economic complexity are affected by noise. Complex. Econ. 3, 1–22 (2014).

    Google Scholar 

  25. 25.

    Pugliese, E., Zaccaria, A. & Pietronero, L. On the convergence of the fitness–complexity algorithm. Eur. Phys. J. Spec. Top. 225, 1893–1911 (2016).

    Article  Google Scholar 

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Acknowledgements

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.

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All the authors contributed equally to the design of the methods. D.M. ran the computations and analysed the data. All authors contributed equally to the writing of the manuscript.

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Correspondence to A. Tacchella.

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Supplementary Information

Supplementary Information, Supplementary Figures 1–4, Supplementary Tables 1–2, Supplementary References 1–13

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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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