Optimizing data-driven weights in multidimensional indexes

Lidia Ceriani, Chiara Gigliarano and Paolo Verme

Economics Letters, 2025, vol. 255, issue C

Abstract: Multidimensional indexes are ubiquitous, and popular, but present non negligible normative choices when it comes to attributing weights to their dimensions. This paper provides a more rigorous approach to the choice of weights by defining a set of desirable properties that weighting models should meet. It shows that Bayesian Networks is the only model across statistical, econometric, and machine learning computational models that meets these properties. An example with EU-SILC data illustrates this new approach highlighting its potential for policies.

Keywords: Multidimensional indexes; Weights; Bayesian Networks (search for similar items in EconPapers)
JEL-codes: C18 C43 I32 (search for similar items in EconPapers)
Date: 2025
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Working Paper: Optimizing Data-driven Weights In Multidimensional Indexes (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:255:y:2025:i:c:s0165176525003362

DOI: 10.1016/j.econlet.2025.112499

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