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Jackknife and Analytical Bias Reduction for Nonlinear Panel Models

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  • Jinyong Hahn
  • Whitney Newey

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 n-super-1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife. Copyright The Econometric Society 2004.

Suggested Citation

  • Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
  • Handle: RePEc:ecm:emetrp:v:72:y:2004:i:4:p:1295-1319
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    File URL: http://hdl.handle.net/10.1111/j.1468-0262.2004.00533.x
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    2. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, July.
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