Specification and testing of some modified count data models

By J. Mullahy. 1986

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, background): “These include hurdle models (Mullahy, 1986; Heilbron, 1989) and zero-inflated count models (Lambert, 1992; Greene, 1994).”
Regression for nonnegative skewed dependent variables (2010, background): “With y > 0 always, Manning and Mullahy (2001) provide guidance on when to prefer OLS or GLM (if e is symmetric and homoskedastic, prefer OLS).”
Determinants of tourist expenditure: a review of microeconometric models (2013, methodology): “A particular version of the two part model is the one used by Alegre et al. (2009) for count data (Mullahy, 1986) who model the number of quarters with positive tourist expenditure.”
A bivariate Poisson count data model using conditional probabilities (2004, background): “Examples of misdispersion can be found in MULLAHY (1986) or HALL, GRILICHES and HAUSMAN (1986).”
Determining heterogeneous behavior for theater attendance (2008, background): “not consider in depth two other major approaches: hurdle models and latent class ( Mullahy 1986 ).”
Growth Curve Models for Zero-Inflated Count Data: An Application to Smoking Behavior (2007, background): “The hurdle, or two-part Poisson model (Heilbron, 1989; King, 1989; Mullahy, 1986) and the ZIP model (Greene, 1994; Lambert, 1992) are two models that have been developed to account for excess zeros.”
Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros (2008, methodology): “The zero-inflated family of models was developed by Mullahy [16] and extended by Lambert [9] and Greene [5].”
Zero-inflated generalized Poisson models with regression effects on the mean, dispersion and zero-inflation level applied to patent outsourcing rates (2007, unknown): “A ZIGP distribution is defined analogously to a ZIP distribution (see Mullahy, 1986) with an additional zero-inflation parameter ω.”
Who cooperates on R&D? (2001, background): “. . See Johnson and Kotz 1969 and Mullahy 1986 as well as . Winkelmann and Zimmermann 1995 for a detailed description of these kinds of model.”
Models for count data with many zeros (1998, methodology): “Heilbron (1994) and Mullahy (1986) discuss other compatible models.”
Number of Accidents or Number of Claims? An Approach with Zero-Inflated Poisson Models for Panel Data (2009, unknown): “A finite mixture models of two distributions combining an indicator distribution for the zero case and a standard count distribution (Mullahy (1986), Lambert (1992)) is a natural candidate to deal with the high number of zero-values.”
Two‐part regression models for longitudinal zero‐inflated count data (2010, methodology): “In this context, an alternative model specification is represented by two-part models (Mullahy, 1986; Lambert, 1992); these are widely used in the economic context since they resemble quite accurately the principal-agent model: homogeneous, observable counted outcomes subject to an exogenous,…”
Space—time zero‐inflated count models of Harbor seals (2007, methodology): “Hence, we consider models that completely specify and separate the binary distribution from the count distribution, which have been called hurdle models or two-stage models in econometrics (Cragg, 1971; Mullahy, 1986; Heilbron, 1994).”
Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data (2021, methodology): “Other models in the literature include the hurdle model (Mullahy, 1986), the two-part model (Heibron, 1994), and the semi-parametric model (Gurmu, 1997).”
Regression Models for Count Data in R (2008, methodology): “Zero-inﬂated models (Mullahy 1986; Lambert 1992) are another model class capable of dealing with excess zero counts (see Cameron and Trivedi 1998, 2005, for an overview).”
Sorting in Experiments with Application to Social Preferences (2019, methodology): “Note that the use of a hurdle model (see Mullahy 1986) to account for the two-step decision—whether to participate and, if participating, how much to give—is not appropriate here.”