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One challenge in the estimation of financial market agent-based models(FABMs) is to infer reliable insights using numerical simulations validated byonly a single observed time series. Ergodicity (besides stationarity) is astrong precondition for any estimation, however it has not been systematicallyexplored and is often simply presumed. For finite-sample lengths and limitedcomputational resources empirical estimation always takes place inpre-asymptopia. Thus broken ergodicity must be considered the rule, but itremains largely unclear how to deal with the remaining uncertainty innon-ergodic observables. Here we show how an understanding of the ergodicproperties of moment functions can help to improve the estimation of (F)ABMs.We run Monte Carlo experiments and study the convergence behaviour of momentfunctions of two prototype models. We find infeasibly-long convergence timesfor most. Choosing an efficient mix of ensemble size and simulated time lengthguided our estimation and might help in general.

Time is limited on the road to asymptopia