  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.
