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.

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Year of publication:	2013
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
Publisher:	Taylor & Francis Journals

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

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