This paper proposes an estimation approach for count data models with endogenous covariates.The maximum approximate composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Irving, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files for the year 2008. The results highlight the importance of accommodating endogeneity effects in count models. In addition, the results reveal the increased propensity for crashes at intersections with flashing lights, intersections with crest approaches, and intersections that are on frontage roads.

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This paper proposes a new econometric formulation and an associated estimation method for multivariate count data that are themselves observed conditional on a participation selection system that takes a multiple discrete-continuous model structure. This leads to a joint model system of a multivariate count and a multiple discrete-continuous selection system in a hurdletype model. The model is applied to analyze the participation and time investment of households in out-of-home activities by activity purpose, along with the frequency of participation in each selected activity. The results suggests that the number of episodes of activities as well as the time investment in those activities may be more of a lifestyle-and lifecycle-driven choice than one related to the availability of opportunities for activity participation