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Bivariate Count Data Regression Using Series Expansions: With Applications

Author

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  • A. Colin Cameron
  • Per Johansson

    (Department of Economics, University of California Davis)

Abstract

Most research on count data regression models, i.e. models for there the dependent variable takes only non-negative integer values or count values, has focused on the univariate case. Very little attention has been given to joint modeling of two or more counts. We propose parametric regression models for bivariate counts based on squared polynomial expansions around a baseline density. The models are more flexible than the current leading bivariate count model, the bivariate Poisson. The models are applied to data on the use of prescribed and nonprescribed medications.

Suggested Citation

  • A. Colin Cameron & Per Johansson, 2004. "Bivariate Count Data Regression Using Series Expansions: With Applications," Working Papers 275, University of California, Davis, Department of Economics.
    • Handle: RePEc:cda:wpaper:275
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    File URL: https://repec.dss.ucdavis.edu/files/GUrNyZXwj4p3BVFozguyZSNp/98-15.pdf
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