Jackknife and Analytical Bias Reduction for Nonlinear Panel Models
Version of Record online: 26 MAY 2004
Volume 72, Issue 4, pages 1295–1319, July 2004
How to Cite
Hahn, J. and Newey, W. (2004), Jackknife and Analytical Bias Reduction for Nonlinear Panel Models. Econometrica, 72: 1295–1319. doi: 10.1111/j.1468-0262.2004.00533.x
- Issue online: 26 MAY 2004
- Version of Record online: 26 MAY 2004
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.