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Quasi-random Monte Carlo application in CGE systematic sensitivity analysis

Theodoros Chatzivasileiadis

arXiv 27 Sep 2017 · Econometrics · publishedApplied Economics Letters (2018) · 5 citations (OpenAlex)

arXiv:1709.09755 · PDF · DOI · OpenAlex · Extracted main text

Abstract

The uncertainty and robustness of Computable General Equilibrium models can be assessed by conducting a Systematic Sensitivity Analysis. Different methods have been used in the literature for SSA of CGE models such as Gaussian Quadrature and Monte Carlo methods. This paper explores the use of Quasi-random Monte Carlo methods based on the Halton and Sobol' sequences as means to improve the efficiency over regular Monte Carlo SSA, thus reducing the computational requirements of the SSA. The findings suggest that by using low-discrepancy sequences, the number of simulations required by the regular MC SSA methods can be notably reduced, hence lowering the computational time required for SSA of CGE models.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Chatzivasileiadis, Theodoros, Francisco Estrada, Marjan Hofkes, and… (2017) Systematic sensitivity analysis of the full economic impacts of sea level rise self0.87462100%
2Villoria, Nelson B, Paul V Preckel, et al (2017) Gaussian Quadratures vs. Monte Carlo Experiments for Systematic Sensitivity Analysis of Computable General Equilibrium Model Res…0.51121100%
3Chatzivasileiadis, Theodoros, Marjan Hofkes, Onno Kuik, and Richard… (2016) Full economic impacts of sea level rise: loss of productive resources and transport disruptions self0.40511100%
4Caflisch, Russel E (1998) Monte carlo and quasi-monte carlo methods0.40511100%
5Jank, Wolfgang (2005) Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM0.40511100%

Showing the top 5 of 5 scored citations.