Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm
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DOI：
10.1016/j.econmod.2006.07.005
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发表时间：
2007-03
影响因子：
4.7
通讯作者：
Marian Leimbach;O. Edenhofer
中科院分区：
文献类型：
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作者：
Marian Leimbach;O. Edenhofer
将提出一种多区域建模的替代方法。这种方法（称为模块化方法）能够再现传统方法以及修改后的根岸方法获得的结果。我们重新设计了根岸方法，使其适用于外国直接投资引起的技术溢出效应的建模。虽然模块化方法能够完全内化技术溢出的外部效应，从而导致更高的福利收益，但修改后的根岸方法不能完全内化它们。后者是由于部门生产函数的影子价格被用于迭代调整根岸福利权重的过程。在模块化方法下，在动态的多区域框架中寻找均衡解的方法是基于分配的，不同于现有的以价格为导向的方法。我们讨论了底层调整算法的特点，与根岸方法的联合最大化相反，它嵌入在一个分散的优化过程中。数值模型实验的结果将证明这种新方法的优越性。
An alternative approach to multi-region modeling will be presented. This approach (called modular approach) is able to reproduce results obtained with the traditional as well as a modified Negishi approach. We redesigned the Negishi approach to make it applicable in modeling technological spillovers induced by foreign direct investments. While the modular approach is able to completely internalize the external effects from technological spillovers, which result in higher welfare gains, the modified Negishi approach cannot completely internalize them. The latter is due to the fact that shadow prices from sectoral production functions are used in order to feed the process of iteratively adjusting the Negishi welfare weights. Under the modular approach, the way of finding an equilibrium solution in a dynamic and multi-regional framework is allocation-based and differs from existing price-oriented methods. We discuss the characteristics of the underlying adjustment algorithm which, in contrast to the joint maximization of the Negishi approach, is embedded in a decentralized optimization process. Results from numerical model experiments will demonstrate the advantage of this novel approach.