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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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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中文摘要：
在过去几十年中，人们对基于代理的金融市场和更广泛经济模型的兴趣不断增加，这在很大程度上是因为这些模型能够再现一些经验观察到的风格化事实，而这些事实不容易通过更传统的建模方法恢复。然而，基于代理的建模范式面临着越来越多的批评，尤其是对当前验证和校准实践的严格性，其中大多数仍然是定性和程式化的事实驱动。虽然近年来有关定量和数据驱动方法的文献有了显著的扩展，但大多数研究都集中于引入新的校准方法，这些方法既不是以现有替代方法为基准，也不是在其产生的估计质量方面进行严格测试。因此，我们通过一系列计算实验，比较了许多著名的ABM校准方法（既有现有的也有新的），试图确定每种方法各自的优缺点以及所得参数估计的总体质量。我们发现，虽然贝叶斯估计在文献中不太流行，但它始终优于基于目标函数的频繁估计方法，并在许多情况下得到合理的参数估计。尽管如此，我们还发现基于代理的模型校准技术需要进一步发展，以便最终校准大规模模型。因此，我们对未来的研究提出建议。
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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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