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Two-part regression models for longitudinal zero-inflated count data

Two-part regression models for longitudinal zero-inflated count data
Two-part regression models for longitudinal zero-inflated count data
Two-part models are quite well established in the economic literature, since they resemble accurately a principal-agent type model, where homogeneous, observable, counted outcomes are subject to a (prior, exogenous) selection choice. The first decision can be represented by a binary choice model, modeled using a probit or a logit link; the second can be analyzed through a truncated discrete distribution such as a truncated Poisson, negative binomial, and so on. Only recently, a particular attention has been devoted to the extension of two-part models to handle longitudinal data. The authors discuss a semi-parametric estimation method for dynamic two-part models and propose a comparison with other, well-established alternatives. Heterogeneity sources that influence the first level decision process, that is, the decision to use a certain service, are assumed to influence also the (truncated) distribution of the positive outcomes. Estimation is carried out through an EM algorithm without parametric assumptions on the random effects distribution. Furthermore, the authors investigate the extension of the finite mixture representation to allow for unobservable transition between components in each of these parts. The proposed models are discussed using empirical as well as simulated data.
0319-5724
197-216
Alfò, Marco
dad67665-30d4-4e5a-abc7-d1a9005bae7b
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Alfò, Marco
dad67665-30d4-4e5a-abc7-d1a9005bae7b
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e

Alfò, Marco and Maruotti, Antonello (2010) Two-part regression models for longitudinal zero-inflated count data. Canadian Journal of Statistics, 38 (2), 197-216. (doi:10.1002/cjs.10056).

Record type: Article

Abstract

Two-part models are quite well established in the economic literature, since they resemble accurately a principal-agent type model, where homogeneous, observable, counted outcomes are subject to a (prior, exogenous) selection choice. The first decision can be represented by a binary choice model, modeled using a probit or a logit link; the second can be analyzed through a truncated discrete distribution such as a truncated Poisson, negative binomial, and so on. Only recently, a particular attention has been devoted to the extension of two-part models to handle longitudinal data. The authors discuss a semi-parametric estimation method for dynamic two-part models and propose a comparison with other, well-established alternatives. Heterogeneity sources that influence the first level decision process, that is, the decision to use a certain service, are assumed to influence also the (truncated) distribution of the positive outcomes. Estimation is carried out through an EM algorithm without parametric assumptions on the random effects distribution. Furthermore, the authors investigate the extension of the finite mixture representation to allow for unobservable transition between components in each of these parts. The proposed models are discussed using empirical as well as simulated data.

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More information

Published date: May 2010
Organisations: Statistics, Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 342303
URI: http://eprints.soton.ac.uk/id/eprint/342303
ISSN: 0319-5724
PURE UUID: 481394ac-ba97-4b56-a4bb-9b2eff56e0ff

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Date deposited: 21 Aug 2012 10:09
Last modified: 14 Mar 2024 11:49

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Contributors

Author: Marco Alfò
Author: Antonello Maruotti

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