The simultaneous estimation method has overtaken the sequential approach as preferred estimation method for hybrid discrete choice models. Notwithstanding, the computational cost of the simultaneous estimation can still be prohibitive when models get more involved and in such cases sequential estimation can still be a potent option. In previous work we conducted a theoretical analysis that led them to identify a major bias affecting the sequential estimation method and proposed a correction term for the bias induced on the estimated parameters by the variability associated with the latent variables; however, they did not attempt to quantify this induced variability. In this paper, we attempt to determine the nature of the variability induced through the latent variables as well as the viability of relying on the sequential estimation method as an alternative (second-best) estimation tool, for cases when the complexity of the specification makes unfeasible to rely on simultaneous estimation. Our results show that the sequential method behaves in an acceptable way (the bias can be avoided through the correction), when the variability associated with the latent variables is low in comparison with the error term of the discrete choice model. On the contrary, when this variability is considerable the bias correction becomes an intricate matter and we cannot guarantee appropriate results.