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