Uncertainty Quantification and Global Sensitivity Analysis for Economic Models

Daniel Harenberg, Stefano Marelli (), Bruno Sudret () and Viktor Winschel ()
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Stefano Marelli: ETH Zurich, Switzerland
Bruno Sudret: ETH Zurich, Switzerland
Viktor Winschel: ETH Zurich, Switzerland

No 17/265, CER-ETH Economics working paper series from CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich

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.

Keywords: computational techniques; uncertainty quantification; global sensitivity analysis (search for similar items in EconPapers)
JEL-codes: C60 C63 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2017-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Journal Article: Uncertainty quantification and global sensitivity analysis for economic models (2019)
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