All Times ET
Keywords: exponential-family random graph models, maximum pseudo-likelihood, godambe matrix, parametric bootstrap
The reputation of the maximum pseudolikelihood estimator (MPLE) for Exponential Random Graph Models (ERGM) has undergone a drastic change over the past 30 years. While first receiving broad support, mainly due to its computational feasibility and the lack of alternatives, the general opinion started to change with the introduction of approximate maximum likelihood estimator (MLE) methods that became practicable due to increasing computing power and the introduction of MCMC methods. Comparison studies appear to yield contradicting results regarding the preference of these two point estimators, however, there is consensus that the prevailing method to obtain an MPLE's standard error by the inverse Hessian matrix generally underestimates standard errors. We propose replacing the inverse Hessian matrix, which has been used by most of the literature on pseudolikelihood estimation, by an approximation of the Godambe matrix that results in confidence intervals with appropriate coverage rates in our simulation studies.