Uncertainty And Sensitivity In Statistical Data

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.