Often the data that is being analyzed take the form of nonnegative integer (count) values. The new COUNTREG procedure implements count data models that take this discrete nature of data into consideration. The dependent variable in these models is a count that represents various discrete events (such as number of accidents, number of doctor visits, or number of children). The conditional mean of the dependent variable is a function of various covariates. Typically, you are interested in estimating the probability of the number of event occurrences using maximum likelihood estimation. The COUNTREG procedure supports the following types of models:

• Poisson regression

• negative binomial regression with linear (NEGBIN1) and quadratic (NEGBIN2) variance functions (Cameron and Trivedi 1986)

• zero-inflated Poisson (ZIP) model (Lambert 1992)

• zero-inflated negative binomial (ZINB) model