Stochastic simulation with informed rotations of Gaussian quadratures

Davit Stepanyan, Georg Zimmermann and Harald Grethe

No 333249, Conference papers from Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project

Abstract: 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.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Date: 2021
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Journal Article: Stochastic simulation with informed rotations of Gaussian quadratures (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:pugtwp:333249

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