averages and are smooth functions of a parameter theta. This includes log likelihood, quasi-log likelihood, and least squares …-strong, and strong identification. We determine the asymptotic size (in a uniform sense) of standard t and quasi-likelihood ratio …

averages and are smooth functions of a parameter theta. This includes log likelihood, quasi-log likelihood, and least squares …-strong, and strong identification. We determine the asymptotic size (in a uniform sense) of standard t and quasi-likelihood ratio …

First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling … finite sample peformance and asymptotics. FDML uses the Gaussian likelihood function for first differenced data and parameter … estimation is based on the whole domain over which the log-likelihood is defined. However, extending the domain of the likelihood …

This paper studies second-order properties of the empirical likelihood overidentifying restriction test to check the … validity of moment condition models. We show that the empirical likelihood test is Bartlett correctable and suggest second …-order refinement methods for the test based on the empirical Bartlett correction and adjusted empirical likelihood. Our second …

finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, GMM and … system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large … FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the …

This paper provides a first order asymptotic theory for generalized method of moments (GMM) estimators when the number … certain regularity conditions, the GMM estimators are shown to converge in probability but not necessarily to the true … parameter, and conditions for consistent GMM estimation are given. A general framework for the GMM limit distribution theory is …