Modeling heaped count data

Tammy H. Cummings (), James W. Hardin (), Alexander C. McLain (), James R. Hussey (), Kevin J. Bennett () and Gina M. Wingood ()
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Tammy H. Cummings: University of South Carolina
James W. Hardin: University of South Carolina
Alexander C. McLain: University of South Carolina
James R. Hussey: University of South Carolina
Kevin J. Bennett: University of South Carolina
Gina M. Wingood: Emory University

Stata Journal, 2015, vol. 15, issue 2, 457-479

Abstract: We present motivation and new commands for modeling heaped count data. These data may appear when subjects report counts that are rounded or favor multiples (digit preference) of a certain outcome, such as the number of cigarettes reported. The new commands for fitting count regression models (Poisson, generalized Poisson, negative binomial) are also accompanied by real-world examples comparing the heaped regression model with the usual regression model as well as the heaped zero-inflated model with the usual zero-inflated model. Copyright 2015 by StataCorp LP.

Keywords: heapcr; ziheapcr; heapr; ziheapr; count data; heaping; Poisson; generalized Poisson; negative binomial; zero-inflation; interval censored; mixture; rescaled (search for similar items in EconPapers)
Date: 2015
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