Simulation based calibration using extended balanced augmented empirical likelihood

This paper introduces an extension of the balanced augmented empirical likelihood (eBAEL) method for calibrating simulation models. We illustrate the efficiency of our method in two simulation studies, where we calibrate moments of different distributions and parameters of a geometric Brownian motion process, comparing our approach against other simulation based methods. In these benchmark experiments we observe converging mean squared errors of the empirical likelihood approach. In fact, the results demonstrate that the eBAEL approach is able to provide the best mean squared errors for calibration and in particular is the most robust calibration method, particularly in the presence of noise.