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On the Use of Spectral Value Decomposition for the Construction of Composite Indices

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  • Farnia, Luca

Abstract

High dimensional composite index makes experts’ preferences in set-ting weights a hard task. In the literature, one of the approaches to derive weights from a data set is Principal Component or Factor Analysis that, although conceptually different, they are similar in results when FA is based on Spectral Value Decomposition and rotation is not performed. This works motivates theoretical reasons to derive the weights of the elementary indicators in a composite index when multiple components are retained in the analysis. By Monte Carlo simulation it offers, moreover, the best strategy to identify the number of components to retain.

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  • Farnia, Luca, 2019. "On the Use of Spectral Value Decomposition for the Construction of Composite Indices," ES: Economics for Sustainability 288457, Fondazione Eni Enrico Mattei (FEEM) > ES: Economics for Sustainability.
    • Handle: RePEc:ags:feemec:288457
    DOI: 10.22004/ag.econ.288457
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    File URL: https://ageconsearch.umn.edu/record/288457/files/NDL2019-008.pdf
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    Cited by:

    1. Luca Farnia & Laura Cavalli & Giula Lizzi & Sergio Vergalli, 2019. "Methodological Insights to Measure the Agenda 2030 at Urban Level in Italy," Sustainability, MDPI, vol. 11(17), pages 1-18, August.
    2. Farnia, Luca & Cavalli, Laura & Vergalli, Sergio, 2019. "Italian Cities SDGs Composite Index: A Methodological Approach to Measure the Agenda 2030 at Urban Level," ES: Economics for Sustainability 291803, Fondazione Eni Enrico Mattei (FEEM) > ES: Economics for Sustainability.

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    Research Methods/ Statistical Methods;

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