An Introduction to Systematic Sensitivity Analysis via Gaussian Quadrature

By C. Arndt. GTAP Technical Paper Series, 2000

162 citations recorded by the source corpus.

Economists recognize that results from simulation models are dependent, sometimes highly dependent, on values employed for critical exogenous variables. To account for this, analysts sometimes conduct sensitivity analysis with respect to key exogenous variables. This paper presents a practical approach for conducting systematic sensitivity analysis, called Gaussian quadrature. The approach views key exogenous variables as random variables with associated distributions. It produces estimates of means and standard deviations of model results while requiring a limited number of solves of the model. Under mild conditions, all of which hold with respect to the GTAP model, there is strong reason to believe that the estimates of means and standard deviations will be quite accurate.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3ccba21a18d5d6d2d7943783a72e62dd51059f4d

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In Management Science and Operations Research

1. A. Yusuf
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3. W. Tyner
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5. Thomas W. Hertel
6. Dileep K. Birur

Citations in the corpus

• On the distributional impact of a carbon tax in developing countries: the case of Indonesia (2015, methodology): “Arndt (1996) has shown that the results using this method are surprisingly accurate, given the relatively modest number of times the model is solved.”
• How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? (2003, unknown): “For this paper, we use the Gaussian Quadrature (GQ) approach to SSA, which has proven to be the most efficient and unbiased approach to systematically assessing the sensitivity of model results to parametric uncertainty (DeVuyst and Preckel, 1997; Arndt, 1996).”
• Applied General Equilibrium Analysis of Agricultural and Resource Policies (2000, methodology): “The good news for AGE modelers is that the Gaussian Quadrature approach to SSA has now been automated for the case of symmetric, independent distributions (Arndt, 1996; Arndt and Pearson, 1996).”
• Commodity Price Volatility in the Biofuel Era: An Examination of the Linkage between Energy and Agricultural Markets (2010, unknown): “3 reports the model- generated standard deviations in annual percentage change in coarse grains prices based on several alternative stochastic simulations undertaken using the Stroud Gaussian Quadrature as detailed in Arndt (1996) and Pearson and Arndt (2000). In the fi rst column, we report the sta”
• Biofuels for all? Understanding the Global Impacts of Multinational Mandates (2008, methodology): “Here, we follow the implementations by Arndt (1996) and Pearson and Arndt (2000).”
• Validating Energy-Oriented CGE Models (2009, methodology): “In addition, it can be decomposed to identify the individual sources of model-based demand response (from firms, households, government, and investment in each and every region of the world).10 Since the goal of this work is to validate the model for energy, and as indicated by usage shares, no ener”