A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Econometric Reviews.
Volume (Year): 27 (2008)
Issue (Month): 4-6 ()
Contact details of provider:
Web page: http://www.tandfonline.com/LECR20
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.