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...

This paper compares generalized method of moments (GMM) and simulated maximum likeli- hood (SML) approaches to the estimation of the panel probit model. Both techniques circum- vent multiple integration of joint density functions without the need to restrict the error term variance-covariance...

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...

This paper compares generalized method of moments (GMM) and simulated maximum likeli- hood (SML) approaches to the estimation of the panel probit model. Both techniques circumvent multiple integration of joint density functions without the need to restrict the error term variance-covariance...