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Uncertainty And Sensitivity In Statistical Data

Author

Listed:
  • Claudiu Vaida-Muntean

    (Bucharest University of Economic Studies)

  • Vergil Voineagu

    (Bucharest University of Economic Studies)

  • Gabriela Munteanu

    (Bucharest University of Economic Studies)

Abstract

Uncertainty and sensitivity analysis in statistical data is considered a necessary requirement in current statistical and econometric practice. Composite indicator development involves stages where subjective judgements have to be made: the selection of individual indicators, the treatment of missing values, the choice of aggregation model, the weights of the indicators, etc. All these subjective choices are the bones of the composite indicator and, together with the information provided by the data themselves, shape the message communicated by the composite indicator. Since the quality of a model depends on the soundness of its assumptions, good modelling practice requires an evaluation of the confidence in the model, assessing the uncertainties associated with the modelling process and the subjective choices taken. This is nothing but sensitivity analysis studying the relationship between information flowing in and out of the model. The main purpose of sensitivity analysis is to highlight how the variation in the output can be apportioned, quantitatively, to different sources of variation in the assumptions, and how the given composite indicator depends upon the information fed into it. Sensitivity analysis is closely related to implicit uncertainty analysis of the model. A combination of uncertainty and sensitivity analysis can help to gauge the robustness of the composite indicator ranking, to increase its transparency, to identify which countries are favoured or weakened under certain assumptions. In econometric practice, a priori assumptions have to be verified a posteriori, with adequate statistical data. In what follows, we shall describe how to apply uncertainty and sensitivity analysis to composite indicators, which has proven to be useful in dissipating some of the controversy.

Suggested Citation

  • Claudiu Vaida-Muntean & Vergil Voineagu & Gabriela Munteanu, 2014. "Uncertainty And Sensitivity In Statistical Data," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 62(12), pages 29-36, December.
  • Handle: RePEc:rsr:supplm:v:62:y:2014:i:12:p:29-36
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    Cited by:

    1. Diana Soare (Dumitrescu), 2015. "Indicators calculated for Competitiveness Operational Programme," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 5(4), pages 140-145, October.
    2. Vergil VOINEAGU & Michal BALOG & Daniel DUMITRESCU & Diana SOARE (DUMITRESCU), 2016. "Managing Financial Instruments by Development Bank of Romania," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(1), pages 32-37, January.
    3. Ziwei Shu & Ramón Alberto Carrasco & Javier Portela García-Miguel & Manuel Sánchez-Montañés, 2022. "Multiple Scenarios of Quality of Life Index Using Fuzzy Linguistic Quantifiers: The Case of 85 Countries in Numbeo," Mathematics, MDPI, vol. 10(12), pages 1-28, June.

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