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Finite sample inference for GMM estimators in linear panel data models

Author

Listed:
  • Stephen Bond

    (Institute for Fiscal Studies and Nuffield College, Oxford)

  • Frank Windmeijer

    (Institute for Fiscal Studies and University of Bristol)

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.

Suggested Citation

  • Stephen Bond & Frank Windmeijer, 2002. "Finite sample inference for GMM estimators in linear panel data models," CeMMAP working papers CWP04/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:04/02
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    File URL: http://cemmap.ifs.org.uk/wps/cwp0204.pdf
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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