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In this article, we propose an integrated calibration and identification method that can identify kinematic and dynamic parameters at the same time. Only a series of static experiments are required in this method, which can significantly simplify the experimental operation and improve the accuracy of identification. A novel calibration model based on axis configuration space and adjoint error model considering joint compliance with only position measurements is developed and both joint compliance and joint zero-offset errors are included in the proposed model. Identifiability analysis shows which joint compliance can be identified and the relationships between the identifiability of joint compliance and the number of measurement points. A recursive algorithm considering physical consistency is newly derived to obtain the derivatives of estimated inertial matrix and gravity vector with respect to joint twists. In this way, the coupling between kinematic and dynamic model is solved and the optimal kinematic and dynamic parameters can be derived using gradient-based optimizer. Comparative study with experiments are reported to show the effectiveness of our algorithm.