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

Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation, the effects are examined in terms of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification and coefficient restrictions show serious deficiencies. The variance of the individual effects is shown to be a major determinant of the poor quality of most asymptotic approximations; therefore, the accurate estimation of this nuisance parameter is investigated. A modification of GMM is found to have some potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample is not too small. Finally, all techniques are employed to actual data and lead to insights which differ considerably from those published earlier