Uncertainty Quantification and Global Sensitivity Analysis for Economic Models
CER-ETH - Center of Economic Research at ETH Zurich, Working Paper 17/265, January 2017
45 Pages Posted: 31 Jan 2017
Date Written: January 13, 2017
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
JEL Classification: C60, C63
Suggested Citation: Suggested Citation