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Open AccessArticle

Recovering Matrices of Economic Flows from Incomplete Data and a Composite Prior

University of Oviedo, Department of Applied Economics, Faculty of Economics, Campus del Cristo, Oviedo, 33006, Spain
Entropy 2010, 12(3), 516-527; https://doi.org/10.3390/e12030516
Received: 3 December 2009 / Accepted: 1 March 2010 / Published: 12 March 2010
(This article belongs to the Special Issue Information and Entropy)
In several socioeconomic applications, matrices containing information on flows-trade, income or migration flows, for example–are usually not constructed from direct observation but are rather estimated, since the compilation of the information required is often extremely expensive and time-consuming. The estimation process takes as point of departure another matrix which is adjusted until it optimizes some divergence criterion and simultaneously is consistent with some partial information-row and column margins–of the target matrix. Among all the possible criteria to be considered, one of the most popular is the Kullback-Leibler divergence [1], leading to the well-known Cross-Entropy technique. This paper proposes the use of a composite Cross-Entropy approach that allows for introducing a mixture of two types of a priori information–two possible matrices to be included as point of departure in the estimation process. By means of a Monte Carlo simulation experiment, we will show that under some circumstances this approach outperforms other competing estimators. Besides, a real-world case with a matrix of interregional trade is included to show the applicability of the suggested technique. View Full-Text
Keywords: cross-entropy estimation; data-weighted priors; matrices of flows; economic applications cross-entropy estimation; data-weighted priors; matrices of flows; economic applications
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Fernández-Vázquez, E. Recovering Matrices of Economic Flows from Incomplete Data and a Composite Prior. Entropy 2010, 12, 516-527.

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