Quantifying the Impact of Unobserved Heterogeneity on Inference from the LogisticModel

摘要

While consequences of unobserved heterogeneity such as biased estimates of binary response regression models are generally known; quantifying these and awareness of situations with more serious impact on inference is however, remarkably lacking. This study examines the effect of unobserved heterogeneity on estimates of the standard logistic model. An estimate of bias was derived for the maximum likelihood estimator βˆ, and simulated data was used to investigate a range of situations that influence size of bias due to unobserved heterogeneity. It was found that the position of the probabilities, along the logistic curve, and the variance of the unobserved heterogeneity, were important determinants of size of bias.