A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts
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 esti...
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| Veröffentlicht in: | Econometric reviews 2008-07, Vol.27 (4-6), p.574-595 |
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| container_issue | 4-6 |
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| container_title | Econometric reviews |
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| creator | Bhati, Avinash Singh |
| description | 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. |
| doi_str_mv | 10.1080/07474930801960451 |
| format | Article |
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| ispartof | Econometric reviews, 2008-07, Vol.27 (4-6), p.574-595 |
| issn | 0747-4938 1532-4168 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_34587738 |
| source | RePEc; Business Source Complete |
| subjects | Chicago Illinois Count outcomes Economic models Generalized Cross-Entropy estimation Homicide rate Information Maximum entropy method Simulation Spatial processes Studies United States Unobserved heterogeneity |
| title | A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts |
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