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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References
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).
Long-Term Projections of Asian GDP and Trade (Asian Development Bank, 2011).
World Economic Outlook. Subdued Demand: Symptoms and Remedies (IMF, 2016).
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).
Little, D. Varieties of Social Explanation (Westview, Boulder, CO, 1991).
Lorenz, E. N. Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci. 26, 636–646 (1969).
Ye, H. et al. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proc. Natl Acad. Sci. USA 112, E1569–E1576 (2015).
Calude, C. S. & Longo, G. The deluge of spurious correlations in big data. Found. Sci. 22, 595–612 (2017).
Hausmann, R. & Hidalgo, C. A. The network structure of economic output. J. Econ. Growth 16, 309–342 (2011).
Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A. & Pietronero, L. A new metrics for countries’ fitness and products’ complexity. Sci. Rep. 2, 723 (2012).
Di Clemente, R., Chiarotti, G. L., Cristelli, M., Tacchella, A. & Pietronero, L. Diversification versus specialization in complex ecosystems. PLoS One 9, e112525 (2014).
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).
Zaccaria, A., Cristelli, M., Tacchella, A. & Pietronero, L. How the taxonomy of products drives the economic development of countries. PLoS One 9, e113770 (2014).
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).
Cristelli, M., Tacchella, A. & Pietronero, L. The heterogeneous dynamics of economic complexity. PLoS One 10, e0117174 (2015).
Balassa, B. Trade liberalisation and “revealed” comparative advantage. Manch. Sch. 33, 99–123 (1965).
Pritchett, L. & Summers, L. H. Asiaphoria Meets Regression to the Mean NBER Working Paper 20573 (National Bureau of Economic Research, 2014).
Dreher, A., Marchesi, S. & Vreeland, J. R. The political economy of IMF forecasts. Public Choice 137, 145–171 (2008).
Batchelor, R. How useful are the forecasts of intergovernmental agencies? the IMF and OECD versus the consensus. Appl. Econ. 33, 225–235 (2001).
Frenkel, M., Rülke, J.-C. & Zimmermann, L. Do private sector forecasters chase after IMF or OECD forecasts? J. Macroecon. 37, 217–229 (2013).
Pugliese, E., Chiarotti, G. L., Zaccaria, A. & Pietronero, L. Complex economies have a lateral escape from the poverty trap. PLoS One 12, e0168540 (2017).
Solow, R. M. A contribution to the theory of economic growth. Q. J. Econ. 70, 65–94 (1956).
Pugliese, E., Napolitano, L., Zaccaria, A. & Pietronero, L. Coherent diversification in corporate technological portfolios. Preprint at https://arXiv.org/abs/1707.02188 (2017).
Battiston, F., Cristelli, M., Tacchella, A. & Pietronero, L. How metrics for economic complexity are affected by noise. Complex. Econ. 3, 1–22 (2014).
Pugliese, E., Zaccaria, A. & Pietronero, L. On the convergence of the fitness–complexity algorithm. Eur. Phys. J. Spec. Top. 225, 1893–1911 (2016).
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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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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DOI: https://doi.org/10.1038/s41567-018-0204-y
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