Finite Sample Inference for GMM Estimators in Linear Panel Data Models

Abstract

We compare the finite sample performance of a range of tests of linear restrictions for linear panel data models estimated using Generalised Method of Moments (GMM). These include standard asymptotic Wald tests based on one-step and two-step GMM estimators; two bootstrapped versions of these Wald tests; a version of the two-step Wald test that uses a more accurate asymptotic approximation to the distribution of the estimator; the LM test; and three criterion-bases tests that have recently been proposed. We consider both the AR(1) panel model, and a design with predetermined regressors. The corrected two-step Wald test performs similarly to the standard one-step Wald test, whilst the bootstrapped one-step Wald test, the LM test, and a simple criterion-difference test can provide more reliable finite sample inference in some cases.