Data disaggregation procedures within a maximum entropy framework
AbstractThe aim of this paper is to formulate an analytical-informational-theoretical approach which, given the incomplete nature of the available micro-level data, can be used to provide disaggregated values of a given variable. A functional relationship between the variable to be disaggregated and the available variables/indicators at the area level is specified through a combination of different macro- and micro-data sources. Data disaggregation is accomplished by considering two different cases. In the first case, sub-area level information on the variable of interest is available, and a generalized maximum entropy approach is employed to estimate the optimal disaggregate model. In the second case, we assume that the sub-area level information is partial and/or incomplete, and we estimate the model on a smaller scale by developing a generalized cross-entropy-based formulation. The proposed spatial-disaggregation approach is used in relation to an Italian data set in order to compute the value-added per manufacturing sector of local labour systems within the Umbria region, by combining the available micro/macro-level data and by formulating a suitable set of constraints for the optimization problem in the presence of errors in micro-aggregates.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.
Volume (Year): 37 (2010)
Issue (Month): 11 ()
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- Rosa Bernardini Papalia, 2011. "An information theoretic approach to ecological inference in presence of spatial heterogeneity and dependence," ERSA conference papers ersa11p317, European Regional Science Association.
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