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Regression models for bivariate count outcomes

Author

Listed:
  • Xinling Xu

    (University of South Carolina)

  • James W. Hardin

    (University of South Carolina)

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.

Suggested Citation

  • Xinling Xu & James W. Hardin, 2016. "Regression models for bivariate count outcomes," Stata Journal, StataCorp LP, vol. 16(2), pages 301-315, June.
    • Handle: RePEc:tsj:stataj:y:16:y:2016:i:2:p:301-315
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    Cited by:

    1. David B. Audretsch & Albert N. Link & Martijn Hasselt, 2019. "Knowledge begets knowledge: university knowledge spillovers and the output of scientific papers from U.S. Small Business Innovation Research (SBIR) projects," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1367-1383, December.
    2. Chiou, Yu-Chiun & Wu, Kuo-Chi, 2024. "Bikesharing: The first- and last-mile service of public transportation? Evidence from an origin–destination perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 187(C).

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