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

16 Pages Posted: 11 May 2019

See all articles by Luca L. Farnia

Luca L. Farnia

Fondazione Eni Enrico Mattei (FEEM)

Date Written: May 10, 2019

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

JEL Classification: C38, C43, C15

Suggested Citation

Farnia, Luca L., On the Use of Spectral Value Decomposition for the Construction of Composite Indices (May 10, 2019). FEEM Working Paper No. 8.2019, Available at SSRN: https://ssrn.com/abstract=3386080 or http://dx.doi.org/10.2139/ssrn.3386080

Luca L. Farnia (Contact Author)

Fondazione Eni Enrico Mattei (FEEM) ( email )

C.so Magenta 63
Milano, 20123
Italy

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