Jackknife Bias Reduction for Nonlinear Dynamic Panel Data Models with Fixed Effects

Content Provider	Semantic Scholar
Author	Dhaene, Geert Jochmans, Koen Thuysbaert, Bram
Copyright Year	2006
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 =2 5and T =8 , the jackknife is very effective in reducing the bias of the MLE and has smaller mean squared error.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ecore.be/Papers/1177063689.pdf
Alternate Webpage(s)	http://www.esg.ac.uk/papers/dhaene.pdf
Alternate Webpage(s)	http://web.mit.edu/econometrics/Extended_Abstract.pdf
Alternate Webpage(s)	https://ifin.usi.ch/assets/docs/seminars/dhaene_28feb2006.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article