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<p>Advances in data availability and statistical inference techniques have given rise to the possibility of a stronger empirical foundation for macroeconomic concepts. Such techniques offer an independent test of macroeconomic theory and enable the systematic discovery of new macroeconomic factors. Using as example the Economic Complexity Index (ECI) proposed by Hidalgo &amp; Hausmann (<CitationRef CitationID="CR13">2009</CitationRef>), we investigate the evidential standards used to empirically confirm and justify new macroeconomic quantities. We compare ECI to a purely data-driven index derived using the tools of causal representation learning and show that ECI can easily be out-performed <i>using its standard</i>. However, this standard (within sample predictive accuracy) is dubious, both when aiming at predictive success or when the aim is policy guidance. A more careful assessment shows that ECI has poor out-of-sample performance and that its policy advice is matched by a simple principal component model. While our assessment of ECI is largely negative, the techniques and standards we develop provide a general framework to systematically assess and empirically justify new macroeconomic quantities.</p>

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Towards an Empirical Foundation of Macro-economic Concepts: Causal Representation Learning Applied to Economic Complexity

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Patrick Burauel,
  • Justin Y. Hong,
  • Frederick Eberhardt

摘要

Advances in data availability and statistical inference techniques have given rise to the possibility of a stronger empirical foundation for macroeconomic concepts. Such techniques offer an independent test of macroeconomic theory and enable the systematic discovery of new macroeconomic factors. Using as example the Economic Complexity Index (ECI) proposed by Hidalgo & Hausmann (2009), we investigate the evidential standards used to empirically confirm and justify new macroeconomic quantities. We compare ECI to a purely data-driven index derived using the tools of causal representation learning and show that ECI can easily be out-performed using its standard. However, this standard (within sample predictive accuracy) is dubious, both when aiming at predictive success or when the aim is policy guidance. A more careful assessment shows that ECI has poor out-of-sample performance and that its policy advice is matched by a simple principal component model. While our assessment of ECI is largely negative, the techniques and standards we develop provide a general framework to systematically assess and empirically justify new macroeconomic quantities.