Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/211167 
Autor:innen: 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
Working Paper No. 008.2019
Verlag: 
Fondazione Eni Enrico Mattei (FEEM), Milano
Zusammenfassung: 
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.
Schlagwörter: 
Composite Index
Weighting
Correlation Matrix
Principal Com-ponent
Factor Analysis
JEL: 
C38
C43
C15
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
495.44 kB





Publikationen in EconStor sind urheberrechtlich geschützt.