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GTAP Resource #6259

"Stochastic simulation with informed rotations of Gaussian quadratures"
by Stepanyan, Davit, Georg Zimmermann and Harald Grethe

Given the fast growth of available computational capacities and the increasing complexity of simulation models addressing agro-environmental issues, uncertainty analysis using stochastic techniques has become a standard modeling practice. However, conventional uncertainty/sensitivity analysis methods are either computationally demanding (Monte Carlo-based methods) or produce results with varying quality (Gaussian quadratures). In this article, we present a computationally inexpensive and reliable uncertainty analysis method for simulation models called informed rotations of Gaussian quadratures (IRGQ). We also provide a linear programming model that generates IRGQ points based on the required input data. The results demonstrate that this method is able to produce approximations that are close to the estimated benchmarks at low computational costs. The method is tested in three different simulation models using different input data in order to demonstrate the independence of the proposed method on specific model types and data structures. This is a methodological paper for practitioners rather than theorists.

Resource Details (Export Citation) GTAP Keywords
Category: 2021 Conference Paper
Status: Not published
By/In: Presented during the 24th Annual Conference on Global Economic Analysis (Virtual Conference)
Date: 2021
Created: Stepanyan, D. (4/14/2021)
Updated: Stepanyan, D. (6/24/2021)
Visits: 1,029
- Model validation and sensitivity analysis
- Software and modeling tools

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