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.
Belloc, F., Bernardi, M., Maruotti, A., Petrella, L. (2013). A Dynamic Hurdle Model for Zero-Inflated Panel Count Data. APPLIED ECONOMICS LETTERS, 20(9), 837-841 [10.1080/13504851.2012.750447].
A Dynamic Hurdle Model for Zero-Inflated Panel Count Data
Belloc F.;
2013-01-01
Abstract
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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https://hdl.handle.net/11365/1064486