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Optimality Between Time of Estimation and Reliability of Model Results in the Monte Carlo Method: A Case for a CGE Model
Computational Economics ( IF 2 ) Pub Date : 2021-01-03 , DOI: 10.1007/s10614-020-10080-8
Tetsuji Tanaka , Jin Guo , Naruto Hiyama , Baris Karapinar

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.



中文翻译:

蒙特卡罗方法中估计时间与模型结果可靠性之间的最优性:以CGE模型为例

可计算的一般均衡(CGE)是政策决策过程中最常用的宏观经济模型之一。经济学家采用蒙特卡罗(MC)方法将随机概念引入确定性CGE模型,以识别加剧全球粮食不安全的气候变化或极端天气模式的影响。但是,MC方法的一个弱点是它耗时的过程,需要使用大量随机抽取来近似概率分布。建模人员不可避免地要在计算持续时间和模型结果的准确性之间进行权衡。本文探讨了CGE分析中两个元素之间的最佳平衡点。假设2000个重复的模拟产生了足够精确的模拟结果,我们比较了100个模型结果,通过2000次重复计算得出500和1000次迭代。我们发现,1000次迭代表明结果可信度高,500次仿真可以很好地发挥作用。但是,尽管准确性适中,但100次计算显然不足以获得可靠的结果。

更新日期:2021-01-12
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