A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts

Avinash Bhati ()

Econometric Reviews, 2008, vol. 27, issue 4-6, 574-595

Abstract: 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.

Keywords: Count outcomes; Generalized Cross-Entropy estimation; Homicide rate; Spatial processes; Unobserved heterogeneity (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (2)

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DOI: 10.1080/07474930801960451

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