Jackknife and analytical bias reduction for nonlinear panel models

No CWP17/03, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed effects, as well as model parameters. We find large bias reductions from using these approaches in examples. We consider asymptotics where T grows with n, as an approximation to the properties of the estimators in econometric applications. We show that if T grows at the same rate as n the fixed effects estimator is asymptotically biased, so that asymptotic confidence intervals are incorrect, but that they are correct for the panel jackknife. We show T growing faster than n1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife.

Pages: 20 pp.
Date: 2003-10-01
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Jackknife and Analytical Bias Reduction for Nonlinear Panel Models (2004)
Working Paper: Jackknife and analytical bias reduction for nonlinear panel models (2003)
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