Count Data Models with Variance of Unknown Form: An Application to a Hedonic Model of Worker Absenteeism
比较了三种参数化条件方差的估计方法与半参数广义最小二乘法在工人缺勤计数模型中的表现，发现系数估计对回归变量列表敏感，但对估计技术不敏感。
We examine an econometric model of counts of worker absences due to illness in a sluggishly adjusting hedonic labor market. We compare three estimators that parameterize the conditional variance?least squares, Poisson, and negative binomial pseudo maximum likelihood?to generalized least squares (GLS) using nonparametric estimates of the conditional variance. Our data support the hedonic absenteeism model. Semiparametric GLS coefficients are similar in sign, magnitude, and statistical significance to coefficients where the mean and variance of the errors are specified ex ante. In our data, coefficient estimates are sensitive to a regressor list but not to the econometric technique, including correcting for possible heteroskedasticity of unknown form.