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A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts

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  • Avinash Singh Bhati

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.

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  • Avinash Singh Bhati, 2008. "A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 574-595.
    • Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:574-595
    DOI: 10.1080/07474930801960451
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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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    2. Giuseppe Arbia, 2011. "A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association (2007--2011)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 377-395, July.

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