Optimizing Data-driven Weights In Multidimensional Indexes
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917980841967616 |
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| author | Ceriani, Lidia Gigliarano, Chiara Verme, Paolo |
| author_facet | Ceriani, Lidia Gigliarano, Chiara Verme, Paolo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_06012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Data-driven Weights In Multidimensional Indexes Ceriani, Lidia Gigliarano, Chiara Verme, Paolo Econometrics Applications 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. |
| title | Optimizing Data-driven Weights In Multidimensional Indexes |
| topic | Econometrics Applications |
| url | https://arxiv.org/abs/2504.06012 |