Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/85168
Authors: 
Inkmann, Joachim
Year of Publication: 
2000
Series/Report no.: 
CoFE Discussion Paper 00/03
Abstract: 
This paper compares conventional GMM estimators to empirical likelihood based GMM estimators which employ a semiparametric efficient estimate of the unknown distribution function of the data. One-step, two-step and bootstrap empirical likelihood and conventional GMM estimators are considered which are efficient for a given set of moment conditions. The estimators are subject to a Monte Carlo investigation using a specification which exploits sequential conditional moment restrictions for binary panel data with multipli-cative latent effects. Among other findings the experiments show that the one-step and two-step estimators yield coverage rates of confidence intervals below their nominal coverage probabilities. The bootstrap methods improve upon this result.
JEL: 
C33
C35
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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