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A dynamic hurdle model for zeroinflated panel count data


  • Filippo Belloc
  • Mauro Bernardi
  • Antonello Maruotti
  • Lea Petrella


This article proposes an approximate conditional dynamic finite mixture hurdle model for panel count data with excess of zeros and endogenous initial conditions. We provide parameter estimates by using the Expectation-Maximization (EM) algorithm in a Nonparametric Maximum Likelihood (NPML) framework. An application to a unique data set on traffic violation counts of a subpopulation of Italian drivers is given.

Suggested Citation

  • Filippo Belloc & Mauro Bernardi & Antonello Maruotti & Lea Petrella, 2013. "A dynamic hurdle model for zeroinflated panel count data," Applied Economics Letters, Taylor & Francis Journals, vol. 20(9), pages 837-841, June.
  • Handle: RePEc:taf:apeclt:v:20:y:2013:i:9:p:837-841
    DOI: 10.1080/13504851.2012.750447

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