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Dear All, I am work on a relatively large data-set (N=380) and use the probit estimator as the response variable is binary. Once the coefficients have been estimated then this exercise will use the probit model for predictions. In order to check the predictive validity of the estimated model, I intend to apply double cross-validation techniques. This simply means that the sample is split into two parts, S1 and S2, and each of these sub-samples is employed as a validation sample for an estimation performed on the other. Whilst this may sound good, I have some difficulties how I would carry out two tasks of this method. 1. How would I assess the stability of coefficients between the two sub-samples? That is, what sort of parametric statistical (even non-parametric) test do i need to carry out? Does checking involve using the LR Test, the Score tet or something else? 2. How would I examine the (mis)classification error rate in the probit model where the regressand is an indicator? In other words, what kind of checking procedures should I apply to investigate the predictive validty of the model? I would be grateful to you if you would offer me your advice on what do regarding the above points as well as suggest me any references of empirical and theoretical context. Thank you very much indeed for taking the time to consider my request. I look forward to hearing from you. Yours sincerely Panos Papanikolaou UWCM Cardiff Great Britain %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%