Count Data Models with Variance of Unknown Form: An Application to a Hedonic Model of Worker Absenteeism

Abstract

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. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology