Modeling heaped count data

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 (Poiss...

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Bibliographic Details
Published inStata Journal Vol. 15; no. 2; pp. 457 - 479
Main Authors Cummings, Tammy H, Hardin, James W, McLain, Alexander C, Hussey, James R, Bennett, Kevin J, Wingood, Gina M
Format Journal Article
LanguageEnglish
Published 2015
Edition199
Subjects
count data
generalized Poisson
heapcr
heaping
heapr
interval censored
mixture
negative binomial
Poisson
rescaled
Research Methods/Statistical Methods
zero-inflation
ziheapcr
ziheapr
Online AccessGet full text
ISSN1536-8634
DOI10.22004/ag.econ.275942

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Summary: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.
ISSN:1536-8634
DOI:10.22004/ag.econ.275942