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Statistical user simulation is an efficient and effective way to train and test the performance of a (spoken) dialog system. In this paper, we design and evaluate a modular data-driven dialog simulator. We decouple the intentional component of the User Simulator, composed by a Dialog Act Model, a Concept Model and a User Model, from the Error Simulator where an Error Model represents different types of ASR/SLU noisy channel distortion. We test different Dialog Act models and two Error Models against the same dialog manager and compare our results with those of real dialogs obtained using such a dialog manager in the same domain. Our results show on the one hand that finer Dialog Act models achieve increasing levels of accuracy with respect to real user behavior and on the other that data-driven Error Models make task completion times and rates closer to real data.