Title: Jackknife Bias Reduction for Nonlinear Dynamic Panel Data Models with Fixed Effects
Authors: Dhaene, Geert
Jochmans, Koen
Thuysbaert, Bram
Issue Date: Feb-2006
Series Title: Working paper
Abstract: We propose jackknife estimators for nonlinear dynamic panel data models with fixed effects that reduce the asymptotic bias of the maximum likelihood estimator (MLE) from O(T −1) to O(T −2) or smaller. The estimators are linear combinations of the MLE computed from the full panel and the MLE’s computed from two or more shorter subpanels.
The relative lengths of the subpanels determine the order of bias reduction that can be achieved. The jackknife can in a similar manner be applied to correct the score or the likelihood. Preliminary simulation results for the probit and logit binary AR(1) models are very encouraging. Even
in small, short panels such as N = 25 and T = 8, the jackknife is very effective in reducing the bias of the MLE and has smaller mean squared error.
Publication status: published
KU Leuven publication type: IR
Appears in Collections:Research Center of Econometrics, Leuven

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