Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171708
Authors: 
Harenberg, Daniel
Marelli, Stefano
Sudret, Bruno
Winschel, Viktor
Year of Publication: 
2017
Series/Report no.: 
Economics Working Paper Series 17/265
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.
Subjects: 
computational techniques
uncertainty quantification
global sensitivity analysis
JEL: 
C60
C63
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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