The Wooldridge method is based on a simple and novel strategy to deal with the initial values problem in the nonlinear dynamic random-effects panel data models. This characteristic of the method makes it very attractive in empirical applications. However, its finite sample performance is not known as of yet. In this paper we investigate the performance of this method in comparison with an ideal case in which the initial values are known constants, the worst scenario based on exogenous initial values assumption, and the Heckman's reduced-form approximation method which is widely used in the literature. The dynamic random-effects probit and tobit (type1) models are used as the working examples. Various designs of Monte Carlo Experiments with balanced and unbalanced panel data sets, and also two full length empirical applications are provided. The results suggest that the Wooldridge method works very well for the panels with moderately long durations (longer than 5-8 periods). In short panels Heckman's reduced-form approximation is suggested (shorter than 5 periods). It is also found that all methods perform equally well for panels of long durations (longer than 10-15 periods).