Uncertainty quantification and global sensitivity analysis for economic models
Uncertainty quantification and global sensitivity analysis for economic models
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DOI：
10.3982/qe866
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发表时间：
2019-01-01
影响因子：
1.8
通讯作者：
Winschel, Viktor
中科院分区：
文献类型：
--
作者：
Harenberg, Daniel;Marelli, Stefano;Winschel, Viktor
我们提出了一个全球性的敏感性分析，量化参数的不确定性对模型结果的影响。具体而言，我们提出了基于方差分解的Sobol指数，以建立参数和单变量效应的重要性排名，以确定其影响的方向。我们采用最先进的方法，构建一个多项式混沌扩展的模型，Sobol的指数和单变量的影响，然后得到分析，只使用有限数量的模型评估。我们将这种分析应用于标准的真实商业周期模型的几个感兴趣的数量，并将其与传统的局部敏感性分析方法进行比较。结果表明，局部灵敏度分析可能是非常误导，而所提出的方法准确，有效地排名所有参数的重要性，识别相互作用和非线性。
We present a global sensitivity analysis that quantifies the impact of parameter uncertainty on model outcomes. Specifically, we propose variance-decomposition-based Sobol' indices to establish an importance ranking of parameters and univariate effects to determine the direction of their impact. We employ the state-of-the-art approach of constructing a polynomial chaos expansion of the model, from which Sobol' indices and univariate effects are then obtained analytically, using only a limited number of model evaluations. We apply this analysis to several quantities of interest of a standard real-business-cycle model and compare it to traditional local sensitivity analysis approaches. The results show that local sensitivity analysis can be very misleading, whereas the proposed method accurately and efficiently ranks all parameters according to importance, identifying interactions and nonlinearities.