Accuracy of method of moments based inference

The generalized method of moments (GMM) is an extremely popular estimation technique in empirical work, since achieving asymptotically valid and efficient inference relies on only a small set of assumptions being satisfied. The first part of this thesis is concerned with mostly standard GMM based inference in linear dynamic micro panel data models, where the accuracy of asymptotic approximations to the properties of different inferential procedures is examined in the context of a comprehensive simulation design. Next, the analysis extends to implementing weak identification-robust coefficient restriction tests, while allowing the weighting matrix to be based on either centered or uncentered moments. Closely related to weak identification is the issue of underidentification, which is discussed in the final part of this thesis. By simulation the properties of different rank statistics are evaluated in the context of the cross-sectional instrumental variables model.