Optimality Between Time of Estimation and Reliability of Model Results in the Monte Carlo Method: A Case for a CGE Model

Abstract

Computable general equilibrium (CGE) is one of the most frequently utilised macroeconomic models in policy decision-making processes. Economists introduced a stochastic concept to deterministic CGE models using the Monte Carlo (MC) method to identify the effects of climate change or extreme weather patterns that have exacerbated global food insecurity. However, a weakness of the MC method is its time-consuming process to approximate probability distributions with a considerable number of randomised draws. Modellers have unavoidably to face a trade-off between the duration of computation and the accuracy of a model’s results. This paper explores an optimal balance point between the two elements in CGE analysis. Assuming that 2000 repetitive simulations create adequately precise simulation outcomes, we compare model results from 100, 500 and 1000 iterations with those from 2000 repetitive calculations. We found that 1000-time iterations indicate highly credible outcomes, 500-time simulations can function well; however, with moderate accuracy, whereas 100-time calculations are apparently insufficient to obtain reliable outcomes.