Information and Entropy Econometrics—Editor's View

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AIC model selection using Akaike weights (2004, methodology): “…between the true model and the approximating model is given by the Kullback–Leibler (1951) information quantity I( f, g), which is equal to the negative of Boltzmann’s (1877) generalized entropy (for details, see Bozdogan, 1987; Burnham & Anderson, 2002; Golan, 2002; and Sakamoto et al., 1986).”
On Pinsker's and Vajda's Type Inequalities for Csiszár's $f$ -Divergences (2006, background): “extensively in many areas including physics, economics and statistics, are respectively T = D(1 ) and CR = D( ) (see [20, 21]).”
Characterizations of joint distributions, copulas, information, dependence and decoupling, with applications to time series (2006, background): “A class of measures of dependence closely related to the multivariate divergence measures is the class of generalized entropies introduced by Tsallis [75] in the study of multifractals and generalizations of Boltzmann–Gibbs statistics (see also [24, 26, 27]) ρ (q) X1,.”