Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/217135 
Year of Publication: 
2019
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 10 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2019 [Pages:] 1-41
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
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.
Subjects: 
Computational techniques
uncertainty quantification
sensitivity analysis
polynomial chaos expansion
JEL: 
C60
C63
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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