HETEROGENEITY, EXCESS ZEROS, AND THE STRUCTURE OF COUNT DATA MODELS
1997; Wiley; Volume: 12; Issue: 3 Linguagem: Inglês
10.1002/(sici)1099-1255(199705)12
ISSN1099-1255
Autores Tópico(s)Statistical Distribution Estimation and Applications
ResumoJournal of Applied EconometricsVolume 12, Issue 3 p. 337-350 Research Article HETEROGENEITY, EXCESS ZEROS, AND THE STRUCTURE OF COUNT DATA MODELS JOHN MULLAHY, Corresponding Author JOHN MULLAHY jmullahy@facstaff.wisc.edu Department of Preventive Medicine, 1st Floor Bradley Memorial, 1300 University Avenue, University of Wisconsin-Madison, Madison, WI 53706, USA National Bureau of Economic Research, Cambridge, MA 02138, USADepartment of Preventive Medicine, 1st Floor Bradley Memorial, 1300 University Avenue, University of Wisconsin-Madison, Madison, WI 53706, USASearch for more papers by this author JOHN MULLAHY, Corresponding Author JOHN MULLAHY jmullahy@facstaff.wisc.edu Department of Preventive Medicine, 1st Floor Bradley Memorial, 1300 University Avenue, University of Wisconsin-Madison, Madison, WI 53706, USA National Bureau of Economic Research, Cambridge, MA 02138, USADepartment of Preventive Medicine, 1st Floor Bradley Memorial, 1300 University Avenue, University of Wisconsin-Madison, Madison, WI 53706, USASearch for more papers by this author First published: 04 December 1998 https://doi.org/10.1002/(SICI)1099-1255(199705)12:3 3.0.CO;2-GCitations: 86AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract This paper demonstrates that the unobserved heterogeneity commonly assumed to be the source of overdispersion in count data models has predictable implications for the probability structure of such mixture models. In particular, the common observation of excess zeros is a strict implication of unobserved heterogeneity. This result has important implications for using count model estimates for predicting certain interesting parameters. Test statistics to detect such heterogeneity-related departures from the null model are proposed and applied in a health-care utilization example, suggesting that a null Poisson model should be rejected in favour of a mixed alternative. © 1997 John Wiley & Sons, Ltd. Citing Literature Volume12, Issue3May 1997Pages 337-350 RelatedInformation
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