Uncertainty quantification and global sensitivity analysis for economic models
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Date
2017-01Type
- Working Paper
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Abstract
Sensitivity analysis assesses the influence of input parameters on the conclusion of a model. Traditional analysis methods - based on evaluating the model at a reference parameter vector and changing one parameter at a time - are local, linear, and usually do not capture interactions among the parameters. By contrast, the global sensitivity analysis that we present summarizes the parameters’ importance over a range of values, fully capturing nonlinearities and identifying interactions. Specifically, we propose Sobol’ indices, which are based on variance decomposition, and exemplify their use with a standard real business cycle model.
Standard approaches to variance decomposition require a large number of model evaluations. To overcome this, we present the state-of-the-art approach for calculating Sobol’ indices, which is based on building a polynomial representation of the model from a limited number of evaluations. In addition, we use this polynomial representation to evaluate the univariate effects, which are conditional expectation functions that can be interpreted as a robust impact of a parameter on the model conclusions. Show more
Permanent link
https://doi.org/10.3929/ethz-a-010820135Publication status
publishedJournal / series
Economics Working Paper SeriesVolume
Publisher
ETH Zurich, Center of Economic Research (CER-ETH)Subject
Computational techniques; Uncertainty quantification; Global sensitivity analysisOrganisational unit
03877 - Bommier, Antoine / Bommier, Antoine
02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.
03962 - Sudret, Bruno / Sudret, Bruno
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Is previous version of: http://hdl.handle.net/20.500.11850/325548
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