Arbeitspapier

Uncertainty Quantification and Global Sensitivity Analysis for Economic Models

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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.

Language: Englisch

Bibliographic citation: Series: Economics Working Paper Series ; No. 17/265

Classification: Wirtschaft
Mathematical Methods; Programming Models; Mathematical and Simulation Modeling: General
Computational Techniques; Simulation Modeling

Subject: computational techniques
uncertainty quantification
global sensitivity analysis
Wirtschaftsmodell
Wahrscheinlichkeitsrechnung
Sensitivitätsanalyse

Event: Geistige Schöpfung

(who): Harenberg, Daniel
Marelli, Stefano
Sudret, Bruno
Winschel, Viktor

Event: Veröffentlichung

(who): ETH Zurich, CER-ETH - Center of Economic Research

(where): Zurich

(when): 2017

DOI: doi:10.3929/ethz-a-010820135

Handle: http://hdl.handle.net/10419/171708

Last update: 10.03.2025, 11:42 AM CET

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Object type

Arbeitspapier

Associated

Harenberg, Daniel
Marelli, Stefano
Sudret, Bruno
Winschel, Viktor
ETH Zurich, CER-ETH - Center of Economic Research

Time of origin

2017

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