A dynamic hurdle model for zeroinflated panel count data
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.
Year of publication: |
2013
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Authors: | Belloc, Filippo ; Bernardi, Mauro ; Maruotti, Antonello ; Petrella, Lea |
Published in: |
Applied Economics Letters. - Taylor & Francis Journals, ISSN 1350-4851. - Vol. 20.2013, 9, p. 837-841
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Publisher: |
Taylor & Francis Journals |
Saved in:
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