Abstract The analysis of uncertainty in large-scale agricultural economic models has gained attention from a policymakers and researchers’ viewpoint. The different methodologies available vary depending on data availability and the nature of the variables subject to analysis, which in turn influences the outcomes. When evaluating the results of previously applied partial stochastic methodologies to partial equilibrium models, underperformance and generation of biases have been observed. This paper evaluates different stochastic methods for introducing yield and macroeconomic uncertainty in a large-scale agricultural economic model and proposes a new methodology for partial uncertainty analysis consisting of a combination of parametric and non-parametric estimators chosen to minimize the statistical prediction error and distributional assumptions. Results suggest that the best methodologies are those relaxing distributional assumptions and allowing for a better representation of historical variability. For uncertainty extraction, the cubic polynomial (for yields) and the multivariate vector auto-regression (for macroeconomic variables) methods perform best. For uncertainty simulation, tests favor semi-parametric methods against parametric approaches. These methods are applied to the ex-ante analysis of global agricultural commodity markets and supplement the traditional deterministic analysis with a statistical representation of stochastic uncertainty.

中文翻译：

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在大规模农业经济模型中引入不确定性：方法论概述

摘要 大规模农业经济模型中的不确定性分析受到政策制定者和研究人员的关注。可用的不同方法因数据可用性和受分析变量的性质而异，这反过来又会影响结果。在评估先前应用于部分均衡模型的部分随机方法的结果时，已观察到性能不佳和偏差的产生。本文评估了在大规模农业经济模型中引入产量和宏观经济不确定性的不同随机方法，并提出了一种新的部分不确定性分析方法，该方法包括选择参数和非参数估计量的组合以最小化统计预测误差和分布假设. 结果表明，最好的方法是那些放宽分布假设并允许更好地表示历史可变性的方法。对于不确定性提取，三次多项式（对于收益率）和多元向量自回归（对于宏观经济变量）方法表现最佳。对于不确定性模拟，测试倾向于半参数方法而不是参数方法。这些方法应用于全球农产品市场的事前分析，并用随机不确定性的统计表示补充了传统的确定性分析。三次多项式（对于收益率）和多元向量自回归（对于宏观经济变量）方法表现最好。对于不确定性模拟，测试倾向于半参数方法而不是参数方法。这些方法应用于全球农产品市场的事前分析，并用随机不确定性的统计表示补充了传统的确定性分析。三次多项式（对于收益率）和多元向量自回归（对于宏观经济变量）方法表现最好。对于不确定性模拟，测试倾向于半参数方法而不是参数方法。这些方法应用于全球农产品市场的事前分析，并用随机不确定性的统计表示补充了传统的确定性分析。