Regression models for bivariate count outcomes

Xinling Xu () and James W. Hardin ()
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Xinling Xu: University of South Carolina
James W. Hardin: University of South Carolina

Stata Journal, 2016, vol. 16, issue 2, 301-315

Abstract: We present a new command, bivcnto, for fitting regression models suitable for analyzing correlated count outcomes. bivcnto allows specification of two correlated count outcomes with either two outcome-specific covariate lists or one common covariate list and fits models using a copula function approach in the general case or using specific parameterizations by Marshall and Olkin (1985, Journal of the American Statistical Association 80: 332–338) or Famoye (2010a, Journal of Applied Statistics 37: 969–981; 2010b, Statistica Neerlandica 64: 112– 124). bivcnto also calculates a likelihood-ratio test comparing the joint model with estimation of two independent outcome-specific models. Copyright 2016 by StataCorp LP.

Keywords: bivcnto; copula function; correlated count data; Poisson; nega- tive binomial; Famoye bivariate Poisson regression; Marshall–Olkin bivariate neg- ative binomial regression; Famoye bivariate negative binomial regression; Famoye bivariate generalized Poisson regression; general bivariate count regression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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