论文标题
时间在渐近道路的道路上受到限制
Time is limited on the road to asymptopia
论文作者
论文摘要
估计金融市场代理模型（FABM）的一个挑战是使用仅通过单个观察到的时间序列验证的数值模拟来推断可靠的见解。对任何估计的巨大性（除平稳性）是一个强大的前提，但是尚未系统地探索它，并且通常简单地被假定。对于有限样本的长度和有限的计算资源，经验估计始终发生在临时性前。因此，必须将损坏的人性化视为规则，但在很大程度上尚不清楚如何处理非共性可观察物中其余的不确定性。在这里，我们展示了对矩功能的千古特性的理解如何有助于改善（f）ABM的估计。我们运行蒙特卡洛实验，研究两个原型模型的矩函数的收敛行为。我们发现大多数人的融合时间不长。选择合奏尺寸和模拟时间长度的有效组合指导了我们的估计，并且通常可能会有所帮助。
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.
