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Factor based Composite Indicators for the Italian Economy


  • Luciana Crosilla

    (Institute for Studies and Economic Analyses)

  • Solange Leproux

    (Institute for Studies and Economic Analyses)

  • Marco Malgarini

    (ISAE - Institute for Studies and Economic Analyses)

  • Francesca Spinelli

    (ISAE - University of Cassino and OCDE)


A factor based approach is often used to build Composite Indicators (CI) from qualitative data stemming from Business and Consumers Survey (BCS). Bruno and Malgarini (2002) and Gayer and Genet (2006) have used factor analysis to synthesize the information contained in the balances of the various surveys Harmonized by the EC (industry, consumers, retail, building and services). However, Marcellino (2006) pointed out that the use of aggregate balance series could imply missing relevant information contained in the surveys. For this reason, in this paper we consider additional information stemming from the percentage of equal answers; moreover, we also use more disaggregate data at the branch level (considering socio-economics characteristics of the respondents for the consumers survey). More specifically, we consider Main Industrial Groupings for the industry survey; small and large multiple shops for the retail survey; building and civil engineering for the construction survey; households and business services for the service survey. Variables to be included in the analysis are pre selected prior to factor extraction on the basis of their contemporaneous or leading/lagging correlation with sector-specific target series. Three methods are then used to extract Composite Indicators, namely Static Principal Component Analysis and Static and Dynamic Factor Analysis (Forni, Hallin, Lippi, Reichlin, 2000, 2001). The various Composite Indicators obtained from the factor based approach are then investigated against the traditional Confidence Indicators in terms of performance with respect to the reference series. As alternative evaluation criteria we use: a) the cross-correlation between the CI and the reference series; b) the directional coherence of movement with the targets; c) turning points analysis (determined applying the Bry-Boschan method). Finally, from the whole set of data stemming from ISAE business and consumers survey we extract aggregate Composite Indicators for the whole Italian economy using the same methods and evaluation criteria outlined above. Indicators calculated with Static Factor Analysis on aggregate balances show the best performance in tracking the reference cycle, i.e. the rate of growth of Italian GDP.

Suggested Citation

  • Luciana Crosilla & Solange Leproux & Marco Malgarini & Francesca Spinelli, 2009. "Factor based Composite Indicators for the Italian Economy," ISAE Working Papers 116, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
  • Handle: RePEc:isa:wpaper:116

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    Cited by:

    1. Inna S. Lola, 2017. "The Statistical Measurement of Business Conditions for Small Entrepreneurs," HSE Working papers WP BRP 71/STI/2017, National Research University Higher School of Economics.

    More about this item


    Business cycle; confidence indicators; factor models; principal components.;

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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