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Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator

Author

Listed:
  • Richard Blundell

    (Institute for Fiscal Studies and University College London)

  • Stephen Bond

    (Institute for Fiscal Studies and Nuffield College, Oxford)

  • Frank Windmeijer

    (Institute for Fiscal Studies and University of Bristol)

Abstract

This chapter reviews developments to improve on the poor performance of the standard GMM estimator for highly autoregressive panel series. It considers the use of the "system" GMM estimator that relies on relatively mild restrictions on the initial condition process. This system GMM estimator encompasses the GMM estimator based on the non-linear moment conditions available in the dynamic error components model and has substantial asymptotic efficiency gains. Simulations, that include weakly exogenous covariates, find large finite sample biases and very low precision for the standard first differenced estimator. The use of the system GMM estimator not only greatly improves the precision but also greatly reduces the finite sample bias. An application to panel production function data for the US is provided and confirms these theoretical and experimental findings.

Suggested Citation

  • Richard Blundell & Stephen Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:00/12
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