文档介绍：© 2003-2005, The Trustees of Indiana University Regression Models for Event Count Data: 1
Regression Models for Event Count Data
Using SAS, STATA, and LIMDEP
Hun Myoung Park
This document summarizes regression models for event count data and illustrates how to
estimate individual models using SAS, STATA, and LIMDEP. Example models were tested in SAS
, STATA , and LIMDEP .
1. Introduction
2. The Poisson Regression Model (PRM)
3. The Negative Binomial Regression Model (NBRM)
4. The Zero-Inflated Poisson Regression Model (ZIP)
5. The Zero-Inflated Negative Binomial Regression Model (ZINB)
6. Conclusion
7. Appendix
1. Introduction
An event count is the realization of a nonnegative integer-valued random variable (Cameron and
Trivedi 1998). Examples are the number of car accidents per month, thunder storms per year, and
wild fires per year. The ordinary least squares (OLS) method for event count data results in
biased, inefficient, and inconsistent estimates (Long 1997). Thus, researchers have developed
various nonlinear models that are based on the Poisson distribution and negative binomial
distribution.
Count Data Regression Models
The left-hand side (LHS) of the equation has event count data. Independent variables are, as in
the OLS, located at the right-hand side (RHS). These RHS variables may be interval, ratio, or
binary (dummy). Table 1 below summarizes the categorical dependent variable regression
models (CDVMs) according to the level of measurement of the dependent variable.
Table 1. Ordinary Least Squares and CDVMs
Model Dependent (LHS) Method Independent (RHS)
Ordinary least Moment based
OLS Interval or ratio
squares method A linear function of
Binary response Binary (0 or 1) interval/ratio or binary
Maximum variables
Ordinal response Ordinal (1st, 2nd , 3rd…)
CDVMs likelihood β+ β X + β X ...
Nominal response Nominal (A, B, C …) me