基于经济主体的模型标定方法比较

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摘要翻译：
在过去的几十年里，对基于代理的金融市场和更广泛经济模型的兴趣一直在增加，这在很大程度上是因为它们能够再现许多经验观察到的风格化事实，而这些事实是更传统的建模方法难以恢复的。然而，基于代理的建模范式面临着越来越多的批评，特别是针对当前验证和校准实践的严格性，其中大多数仍然是定性的和风格化的事实驱动的。虽然关于定量和数据驱动方法的文献近年来有了很大的扩展，但大多数研究都侧重于引入新的校准方法，这些方法既不以现有的替代方法为基准，也不对其所产生的估计数的质量进行严格测试。因此，通过一系列的计算实验，我们比较了一些著名的ABM校准方法，包括已建立的和新的，试图确定每种方法各自的优缺点和结果参数估计的总体质量。我们发现，贝叶斯估计，虽然不太受欢迎的文献，一贯优于频率，目标函数为基础的方法和结果合理的参数估计在许多情况下。尽管如此，我们还发现基于Agent的模型校准技术需要进一步发展，以便对大规模模型进行确定性校准。因此，我们对未来的研究提出了建议。
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英文标题：
《A Comparison of Economic Agent-Based Model Calibration Methods》
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作者：
Donovan Platt
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Interest in agent-based models of financial markets and the wider economy has increased consistently over the last few decades, in no small part due to their ability to reproduce a number of empirically-observed stylised facts that are not easily recovered by more traditional modelling approaches. Nevertheless, the agent-based modelling paradigm faces mounting criticism, focused particularly on the rigour of current validation and calibration practices, most of which remain qualitative and stylised fact-driven. While the literature on quantitative and data-driven approaches has seen significant expansion in recent years, most studies have focused on the introduction of new calibration methods that are neither benchmarked against existing alternatives nor rigorously tested in terms of the quality of the estimates they produce. We therefore compare a number of prominent ABM calibration methods, both established and novel, through a series of computational experiments in an attempt to determine the respective strengths and weaknesses of each approach and the overall quality of the resultant parameter estimates. We find that Bayesian estimation, though less popular in the literature, consistently outperforms frequentist, objective function-based approaches and results in reasonable parameter estimates in many contexts. Despite this, we also find that agent-based model calibration techniques require further development in order to definitively calibrate large-scale models. We therefore make suggestions for future research.
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PDF链接：
https://arxiv.org/pdf/1902.05938

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关键词：Quantitative Contribution Applications alternatives introduction 能够 参数估计 consistently 函数 driven