We introduce three new commands—nop, ziop2, and ziop3—for the estimation of a three-part nested ordered probit model, two-part zero-inflated models Harris Zhao (2007, Journal Econometrics 141: 1073–1099) Brooks, Harris, Spencer (2012, Economics Letters 117: 683–686), model Sirchenko (2020, Studies in Nonlinear Dynamics 24: 1) for ordinal outcomes, with both exogenous endogenous switching. The a...

This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a new zero-inflated count data model that is based on two homogeneous Poisson processes and accounts for exposure time in a theory consistent way. The new model is used in an application to the effect of insurance generosity on the number of absent days. JEL Classification: J29, C25

In recent years, numerous researches have been carried out with purpose of predicting motor vehicle crashes on transportation facilities as freeways and urban or rural highways. Accident process can be modeled successfully with assuming a dual-state data-generating process. Based on this assumption, road components like intersections or road segments have two states of perfectly safe and unsafe...

This paper extends the work of Pimentel et al. (2015), presenting an estimator Kendall’s τ for bivariate zero-inflated count data. We provide achievable bounds our proposed and suggest how to estimate them, thereby making useful in practice.

Due to the expansion of internet, we encounter various types big data such as web documents or sensing data. Compared traditional small experimental samples, provide more chances find hidden and novel patterns with analysis using statistics machine learning algorithms. However, use increases, problems also occur. One them is a zero-inflated problem in structured preprocessed from Most count val...