The Wooldridge method is based on a simple and novel strategy to deal with the initial values problem in nonlinear dynamic random-effects panel data models. The characteristic of the method makes it very attractive in empirical applications. However, its finite sample performance and robustness are not fully known as of yet. In this paper we investigate the performance and robustness of this method in comparison with an ideal case in which the initial values are known constants; the worst scenario is based on an 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 (type I) models are used as working examples. Various designs of the Monte Carlo experiments and two further empirical illustrations are provided. The results suggest that the Wooldridge method works very well only for the panels of moderately long duration (longer than 5-8 periods). Heckman's reduced-form approximation is suggested for short panels (shorter than 5 periods). It is also found that all the methods tend to perform equally well for panels of long duration (longer than 15-20 periods).
