Forecasting the countries’ gross domestic product growth: The case of Technological Fitness

Orazio Angelini, Andrea Gabrielli, Andrea Tacchella, Andrea Zaccaria, Luciano Pietronero and T. Di Matteo

Chaos, Solitons & Fractals, 2024, vol. 184, issue C

Abstract: The cornerstone of Economic Complexity (EC) studies is the assumption that most of the fundamental information about countries’ capabilities can be extracted from the products they export. This extreme dimensionality reduction is evident in the typical models used in EC for Gross Domestic Product Per Capita (GDPpc) forecasting, in which only two dimensions – Economic Fitness (EF) and GDPpc – are considered. In this work, we consider adding a third dimension, Technological Fitness (TF), which is computed from the measured patenting activity of countries. We find this improves the GDPpc forecast by disambiguating the different growth patterns of countries. The effect is clearer for those advanced-development countries that already export most of the products present in customs’ ontologies, saturating along the EF dimension. Importantly, we show that a higher dimensional model is not necessarily better for all countries and at all times. We illustrate a finding that exemplifies this: while for China adding TF information improves the GDPpc predictions, this is not true for India, a country that according to traditional metrics is very similar. We suggest that future work targeted at introducing new information in EC should exercise care in tailoring the observable quantities employed to each country being examined.

Keywords: Dynamical systems; Macroeconomic forecasting; Economic complexity; Fitness; Patents (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924005587

DOI: 10.1016/j.chaos.2024.115006

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