Virtual testing is an essential tool in the analysis of many automotive control concepts and in many case accurate models of the vehicle dynamics are important. Traditional models, as normally used in multi-body dynamics, are usually too complex for this use and too difficult to derive. A solution that is often much faster is to infer estimates of the parameter values from measurements obtained by performing different driving maneuvers with the car. However, most methodologies described in the literature so far are focused on the identification of single vehicle parameters, assuming most other parameters to be known a priori, and often require a sophisticated and expensive test setup. In this paper we show how methods from stochastic subspace identification (SSI), model updating (MU) and direct continuous time system identification (CTSI) can be combined to obtain a fully parametrized model of the vehicle suspension system from scratch, using only data from simple dynamical tests and inexpensive measurement equipment. The newly proposed method is evaluated on a real test car and compared to the performance of a model obtained from static tests. It was found that the model identified using the new method matches the dynamics of both the real car and the model obtained in static tests sufficiently well.