Quasi-random Monte Carlo application in CGE systematic sensitivity analysis

TL;DR

This paper investigates the use of Quasi-random Monte Carlo methods with Halton and Sobol' sequences to enhance efficiency in systematic sensitivity analysis of CGE models, reducing computational time.

Contribution

It introduces Quasi-random Monte Carlo techniques for CGE sensitivity analysis, demonstrating improved efficiency over traditional Monte Carlo methods.

Findings

Reduced number of simulations needed with low-discrepancy sequences

Lower computational time for SSA of CGE models

Effective application of Halton and Sobol' sequences

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.