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Flexible Bivariate Count Data Regression Models

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  • Shiferaw Gurmu
  • John Elder

Abstract

The article develops a semiparametric estimation method for the bivariate count data regression model. We develop a series expansion approach in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components, and in which, unlike existing commonly used models, positive as well as negative correlations are allowed. Extensions that accommodate excess zeros, censored data, and multivariate generalizations are also given. Monte Carlo experiments and an empirical application to tobacco use confirms that the model performs well relative to existing bivariate models, in terms of various statistical criteria and in capturing the range of correlation among dependent variables. This article has supplementary materials online.

Suggested Citation

  • Shiferaw Gurmu & John Elder, 2011. "Flexible Bivariate Count Data Regression Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 265-274, August.
    • Handle: RePEc:taf:jnlbes:v:30:y:2011:i:2:p:265-274
    DOI: 10.1080/07350015.2011.638816
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    Cited by:

    1. Vera Hofer & Johannes Leitner, 2012. "A bivariate Sarmanov regression model for count data with generalised Poisson marginals," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2599-2617, August.
    2. Eugenio Miravete, 2014. "Testing for complementarities among countable strategies," Empirical Economics, Springer, vol. 46(4), pages 1521-1544, June.

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