Abstract

Count data regression is now a well-established tool in econometrics. If the outcome variable is measured as a nonnegative count, y, y ∈ ?₀ = {0, 1, 2, …}, and the object of interest is the marginal impact of a change in the variable x on the regression function E[y|x], then a count regression is a relevant tool of analysis. Because the response variable is discrete, its distribution places probability mass at nonnegative integer values only. Fully parametric formulations of count models accommodate this property of the distribution. Some semiparametric regression models only accommodate y ≥ 0, but not discreteness. Given the discrete nature of the outcome variable, a linear regression is usually not the most efficient method of analyzing such data. The standard count model is a nonlinear regression.