Predicting Probabilities: Inherent and Sampling Variability in the Estimation of Discrete-Choice Models

In this paper, we address two issues: the small sample variability in estimation of the parameters of female labor force participation models with dichotomous dependent variables due to random variation across small samples; and whether large-sample estimates of discrete-choice models yield the same results. In the first case, both logit and profit models exhibit similar patterns of parameter instability in 'small' samples and that a sample size of over 10,000 observations is required to reduce the issue of sampling variability to acceptable levels. In the second case, both models generate similar qualitative results in large samples; however, even in the largest samples, the quantitative results generated by these models diverge significantly. Coauthors are Zhengxi Lin, Lars Osberg, and Shelley Phipps. Copyright 1994 by Blackwell Publishing Ltd