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A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts


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  • Avinash Singh Bhati


This article discusses and applies an information-theoretic framework for incorporating knowledge of the spatial structure in a sample while extracting from it information about processes resulting in count outcomes. The framework, an application of the Generalized Cross-Entropy (GCE) method of estimating count outcome models, allows researchers to incorporate such real-world features as unobserved heterogeneity—with or without spatial clustering—when modeling spatially correlated counts. The information-recovering potential of the approach is investigated using a limited set of simulations. It is then used to study the determinants of counts of homicides recorded in 343 neighborhoods in Chicago, Illinois.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 27 (2008)
Issue (Month): 4-6 ()
Pages: 574-595

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Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:574-595

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Keywords: Count outcomes; Generalized Cross-Entropy estimation; Homicide rate; Spatial processes; Unobserved heterogeneity;


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