Finite sample inference for GMM estimators in linear panel data models

Stephen Bond () and Frank Windmeijer
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Stephen Bond: Institute for Fiscal Studies and Nuffield College, Oxford

No CWP04/02, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

JEL-codes: C12 C23 (search for similar items in EconPapers)
Pages: 45 pp.
Date: 2002-05-01
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Citations: View citations in EconPapers (73)

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Related works:
Working Paper: Finite sample inference for GMM estimators in linear panel data models (2002)
Working Paper: Finite Sample Inference for GMM Estimators in Linear Panel Data Models (2002)
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