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Le componenti principali pesate geograficamente per la definizione di indicatori compositi locali

Author

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  • Alfredo Cartone
  • Paolo Postiglione

Abstract

Il presente lavoro analizza il problema della costruzione degli indicatori compositi a livello locale. Gli indicatori rappresentano sempre pi? un valido strumento di ausilio per la definizione di interventi adeguati di policy che siano basati su un?effettiva analisi della realt?. Un indicatore composito misura concetti multidimensionali che sono difficili da comprendere attraverso l?analisi di una molteplicit? di indicatori semplici. Il problema della sintesi degli indicatori semplici ? un argomento spesso dibattuto nella letteratura specialistica. La tecnica statistica dell?analisi in componenti principali ? uno strumento frequentemente utilizzato per risolvere tale problema. In generale, quando l?unit? statistica di osservazione ? geo-riferita, la versione classica dell?analisi in componenti principali risulta non adeguata per la sintesi di indicatori semplici. Infatti, usando l?analisi in componenti principali standard, vengono trascurati alcuni effetti spaziali che caratterizzano in modo cruciale le unit? che si distribuiscono sul territorio. In particolare, possono essere considerati gli effetti di eterogeneit? e dipendenza spaziale. In questo articolo, gli autori applicano una tecnica di analisi in componenti principali pesata geograficamente che ? stata introdotta recentemente in letteratura. Tale tecnica tiene in debita considerazione l?effetto di eterogeneit? spaziale. La metodologia ? utilizzata al fine della definizione di indicatori compositi di benessere a livello locale. In particolare, il caso di studio riguarda le 110 province italiane per l?anno 2011. I risultati evidenziano come l?eterogeneit? spaziale non possa essere ignorata quando si analizzano dati rilevati su unit? territoriali, e pertanto, l?utilizzo del?l?analisi in componenti principali modificata spazialmente risulta pi? adeguata per lo studio del fenomeno sotto investigazione.

Suggested Citation

  • Alfredo Cartone & Paolo Postiglione, 2016. "Le componenti principali pesate geograficamente per la definizione di indicatori compositi locali," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2016(1), pages 33-52.
    • Handle: RePEc:fan:restre:v:html10.3280/rest2016-001002
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    References listed on IDEAS

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    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    3. Paolo Postiglione & M. Andreano & Roberto Benedetti, 2013. "Using Constrained Optimization for the Identification of Convergence Clubs," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 151-174, August.
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    5. Matteo Mazziotta, Adriano Pareto, 2013. "Methods For Constructing Composite Indices: One For All Or All For One?," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 67-80, April-Jun.
    6. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
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    1. M. Simona Andreano & Roberto Benedetti & Andrea Mazzitelli, 2016. "L?eterogeneit? spaziale nello sviluppo locale in Italia: un?analisi basata sulla costruzione di un indicatore sintetico," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2016(3), pages 9-27.

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    More about this item

    Keywords

    Regressione pesata spazialmente; indicatori compositi; indicatori di benessere; econometria spaziale; funzione kernel;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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