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 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.