Consistent Estimation of Agent-Based Models by Simulated Minimum Distance

Agent-based (AB) models are considered a promising tool for macroeconomic analysis. However, until estimation of AB models become a common practice, they will not get to the center stage of macroeconomics. Two difficulties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, and (ii) the aggregate properties of the model cannot be analytically understood. The first one calls for simulation-based estimation techniques; the second requires additional_x000d_ statistical testing in order to ensure that the simulated quantities are consistent estimators of the theoretical quantities. The possibly high number of parameters involved and the non-linearities in the theoretical quantities used for estimation add to the complexity of the problem. As these difficulties are also shared, though to a different extent, by DSGE models, we first look at the lessons that can be learned from this literature. We identify simulated minimum distance (SMD) as a practical approach to estimation of AB models, and we discuss the conditions which ensure consistency of SMD estimators in AB models.