A finite mixture model for multivariate counts under endogenous selectivity |
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Authors: | Marco Alfò Antonello Maruotti Giovanni Trovato |
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Institution: | 1.Dipartimento di Statistica, Probabilità e Statistiche Applicate,“Sapienza” Università di Roma,Rome,Italy;2.Dipartimento di Istituzioni Pubbliche, Economia e Società,Università di Roma Tre,Rome,Italy;3.Dipartimento di Economia e Istituzioni,Università di Roma “Tor Vergata”,Rome,Italy |
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Abstract: | We describe a selection model for multivariate counts, where association between the primary outcomes and the endogenous selection source is modeled through outcome-specific latent effects which are assumed to be dependent
across equations. Parametric specifications of this model already exist in the literature; in this paper, we show how model
parameters can be estimated in a finite mixture context. This approach helps us to consider overdispersed counts, while allowing
for multivariate association and endogeneity of the selection variable. In this context, attention is focused both on bias
in estimated effects when exogeneity of selection (treatment) variable is assumed, as well as on consistent estimation of
the association between the random effects in the primary and in the treatment effect models, when the latter is assumed endogeneous.
The model behavior is investigated through a large scale simulation experiment. An empirical example on health care utilization
data is provided. |
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Keywords: | |
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