Empirical Likelihood Estimation of Agent-based Models

This paper addresses a major methodological issue of Agent-Based (AB) models which has prevented them from being accepted by the mainstream research community. Any scienti c model or theory has to be testable for example by comparing predictions which are contingent on the model against empirical observations. If they coincide the model can be considered as provisionally true, else it is falsi ed and at least one of the model axioms is false. However, in order to derive testable implications a meaningful calibration and estimation procedure is required, and this issue has been given relatively little attention in the AB literature. This paper introduces and examines an estimation procedure for AB models which is based on a statistical methodology called Empirical Likelihood.