Reducing revisions in short-term business surveys
AbstractTimeliness is a driving feature of national economic statistics, especially in a short-term frame. In a survey sampling context, the current practice normally consists in a data release process based on a first preliminary estimate available for users within a short-time, followed by a final estimate, available when the data capturing process is considered completed. The number of preliminary estimates can be higher than one: for each of them the magnitude of revisions can be evaluated, on the basis of the difference respect to the final estimate. In this context, according to a model based approach, we propose and compare some preliminary estimation techniques aimed at reducing the average revision. After the definition of the optimal preliminary estimation strategy when the potential non-response bias is ignored, the case when potential differences between preliminary and late respondents can not be neglected is considered as well, with the proposal of a particular poststratification procedure. Further, an empirical comparison among various provisional estimation strategies has been carried out on the basis of the quarterly wholesale trade survey carried out by ISTAT (Italian National Statistical Institute) for the period 2003-2006, aimed at estimating quarterly changes of the average turnover. Results show that a proper model specification leads to preliminary estimation techniques characterised by an average revision lower than that got using the actual respondents’ sample mean.
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