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

Luca Farnia
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Luca Farnia: Fondazione Eni Enrico Mattei

No 2019.08, Working Papers from Fondazione Eni Enrico Mattei

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.

Keywords: Composite Index; Weighting; Correlation Matrix; Principal Com-ponent; Factor Analysis (search for similar items in EconPapers)
JEL-codes: C15 C38 C43 (search for similar items in EconPapers)
Date: 2019-05
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:fem:femwpa:2019.08

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