内生供应链转型：基于动态校准的非中性弹性处理网络
Computing Endogenous Transformations in Processing Networks: A Dynamic Calibration Approach
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中文总结 AI 辅助
针对供应链内生转型中的非凸逆优化难题，提出利用上游性拓扑的混合启发式算法，成功校准美国10部门模型弹性，实现供应链转型的完全内生化预测。
中文摘要 AI 辅助
理解供应链如何内生转型，需要一种具有非中性替代弹性的处理网络参数模型。虽然级联CES（CCES）生产函数为这些多层关联提供了严谨的框架，但从时间序列数据中动态校准其结构参数构成一个高度非凸的逆优化问题。强制执行严格的微观经济学凹性约束会导致标准整体方法因极端病态性和维度灾难而失效。为克服这一计算瓶颈，我们提出了一种新颖的利用结构特性的算法。通过利用网络的物理上游性拓扑，我们的混合启发式方法在垂直级联序列下降与水平块坐标下降之间交替进行。我们的框架成功校准了美国一个10部门宏观经济模型的基本弹性，提供了一个可处理的计算引擎，以完全内生化和预测复杂的供应链转型。
英文摘要
Understanding how global supply chains endogenously transform in response to disruptions requires a parametric model of processing networks with non-neutral substitution elasticities. While cascaded CES production functions provide a rigorous analytical framework, dynamically calibrating their structural parameters from time-series data constitutes a highly non-convex inverse optimization problem. Since enforcing strict microeconomic concavity renders standard monolithic algorithmic approaches computationally intractable, we propose a novel data-driven, structure-exploiting algorithm to bypass this limitation. By leveraging the physical upstreamness topology of the supply chain, our hybrid heuristic effectively breaks the curse of dimensionality inherent in economywide processing networks. Applying this computational framework to United States time-series data, we successfully calibrate the fundamental heterogeneous elasticities. This provides a scalable analytics engine to fully endogenize complex supply chain transformations, thereby offering a practical tool to measure structural productivity and uncover the elastic origins of systemic tail risks in large-scale production networks.
发表机构
- Faculty of Economics, Nihon Fukushi University（日本福冈大学经济学部）
- Institute of Economics, Chukyo University（中京大学经济学研究所）
机构由 AI 辅助整理，请以论文原文为准。
