A comparison of economic agent-based model calibration methods

Abstract

Despite significant expansion in recent years, the literature on quantitative and data-driven approaches to economic agent-based model validation and calibration consists primarily of studies that have focused on the introduction of new calibration methods that are neither benchmarked against existing alternatives nor rigorously tested in terms of the quality of the estimates they produce. In response, we compare a number of prominent agent-based model calibration methods, both established and novel, through a series of computational experiments in an attempt to determine the respective strengths and weaknesses of each approach. Overall, we find that a simple, likelihood-based approach to Bayesian estimation consistently outperforms several members of the more popular class of simulated minimum distance methods and results in reasonable parameter estimates in many contexts, with a degradation in performance observed only when considering a large-scale model and attempting to fit a substantial number of its parameters.