The Relative Performance of Poisson and Negative Binomial Regression Estimators
通过抽样实验比较负二项和泊松估计量在有限样本下的表现，发现除非对过度离散形式有明确假设，否则负二项估计量并无明显优势。
Abstract Negative binomial estimators are commonly used in estimating models with count‐data dependent variables. In this paper, sampling experiments are used to evaluate the performance of these estimators relative to the simpler Poisson estimator in finite‐sample situations. The results do not suggest a clear preference for negative binomial estimators in situations in which the underlying dependent variables are overdispersed, unless the researcher is comfortable in assumptions about the precise form of the overdispersion.