Two difficulties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, (ii) the aggregate properties of the model cannot be analytically understood. In this paper we show how to circumvent these difficulties and under which conditions ergodic models can be consistently estimated by simulated minimum distance techniques, both in a long-run equilibrium and during an adjustment phase.
Grazzini, J., Richiardi, M., Estimation of ergodic agent-based models by simulated minimum distance, <<JOURNAL OF ECONOMIC DYNAMICS & CONTROL>>, 2015; 51 (Febbraio): 148-165. [doi:10.1016/j.jedc.2014.10.006] [http://hdl.handle.net/10807/65446]
Estimation of ergodic agent-based models by simulated minimum distance
Grazzini, Jakob;
2015
Abstract
Two difficulties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, (ii) the aggregate properties of the model cannot be analytically understood. In this paper we show how to circumvent these difficulties and under which conditions ergodic models can be consistently estimated by simulated minimum distance techniques, both in a long-run equilibrium and during an adjustment phase.
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