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Non-surveying Construction of a 47 Interregional Input-Output Table and Calibration of SCGE Model

47个区域间投入产出表的非调查构建及SCGE模型的校正

基本信息

批准号：
15560458
负责人：
MIYAGI Toshihiko
金额：
$ 2.37万
依托单位：
Gifu University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15560458/
关键词：
SCGE Model Inter-regional IO Analysis Applied General Equilibrium Analysis 47 Inter-regional IO Table in Japan Calibration Interregional Trade Coefficient Transportation Investment Chenery-Moses Model SAM CGE分析バランシングファクター法 SCGE分析

项目摘要

Main results of this research ate summarized as follows :1)Our approach for constructing interregional input-output table is regarded as a hybrid method in which initial trade coefficients estimated by the existing various social-economical data are successively adjusted to satisfy consistency conditions.2)Our non-surveying technique is based on a balancing-factor method, however, it differs from the conventional balancing-factor method in that our approach should solve linear equation systems for finding one of the balancing-factors.3)Convergence of the algorithm was successfully examined with usage of small size sample data of Japanese Inter-regional IO data. Application of the algorithm to 47-region data was successfully completed with less than one percentage errors between estimated and actual regional products ; however, for some sectors in several regions, the error exceeds more than ten percentages. Since the amounts of products in such sectors are relatively small, we can expe … More ct that those errors can be reduced by sector aggregation and do not have significant effects on further analysis as well.4)Constructed interregional input-out table consists of 45 sectors and 47 regions, of which elements account for as much as more than four hundred millions. For finding the Leontief inverse matrix, we have used GAUSS software package. Interdependence analysis is conducted using the 47-interregional table, and we have found interesting results about the economical iuterdependences among metropolitan areas and surrounding regions.5)We have proposed a new trade model which includes the concept of spatial advantage in terms of economic distance for distinct regions. Spatial advantage are measurable if one can use interregional IO data as the benchmark equilibrium, however, such data are rarely in use in the usual analysis. We have developed the dimension-reduction method in which three-dimensional parameters concerned with spatial advantage is transformed to two-dimensional parameters with assistance of exogenously given parameter on travel times between distinct regions. This method allows us to calibrate trade coefficients without interregional IO data.6)We have developed and examined several calibration procedures corresponding to the benchmark equilibrium data which are easily obtainable. The worst situation may be the case in which no data except the IO data for total region is available. Even in such a case, our approach can produce satisfactory results in constructing interregional IO estimates. If one can use a complete interregional IO date, the method can reproduce completely consistent estimates with given data by using transformation technique of Chenery-Moses model to Isard type.From these results, we can conclude that our approach provides a theoretically consistent and practically feasible SCGE model. Unfortunately, however, we could not implement SCGE analysis using the interregional IO data created in this research, mainly due to time limitation and the needs of solving a large scale of nonlinear equation systems. Since this is not an essential problem and merely a technical one, we are optimistic with respect to that. Less

本研究的主要成果概括如下：1)我们的区域间投入产出表构造方法是一种混合方法，通过对现有的各种社会经济数据估计的初始贸易系数进行逐次调整以满足一致性条件。2)我们的非测量技术基于平衡因子方法，但它与传统的平衡因子方法不同，它需要求解线性方程组来寻找平衡因子之一。3)使用日本区域间IO数据的小样本数据成功地检验了算法的收敛。将该算法应用于47个地区的数据，估计的地区产品与实际的地区产品之间的误差不到1个百分点；然而，对于几个地区的一些部门，误差超过10个百分点。由于这些部门的产品数量相对较少，我们可以使用…4)构建的区域间投入产出表由45个部门和47个区域组成，其中要素所占比例高达4亿以上。为了求出Leontief逆矩阵，我们使用了GAUSS软件包。利用47个区间表进行了相互依赖分析，我们发现了关于大都市区和周边地区之间经济依赖的有趣结果。5)我们提出了一个新的贸易模型，其中包含了不同地区经济距离方面的空间优势的概念。如果可以使用区域间的IO数据作为基准均衡，那么空间优势是可以衡量的，然而，这种数据很少在通常的分析中使用。我们发展了一种降维方法，即借助不同区域之间旅行时间的外来给定参数，将与空间优势有关的三维参数转换为二维参数。这种方法使我们能够在没有区域间IO数据的情况下校准贸易系数。6)我们开发并检查了几个与基准均衡数据相对应的校准程序，这些程序很容易获得。最糟糕的情况可能是除了Total Region的IO数据外没有其他数据可用。即使在这种情况下，我们的方法也可以在构建区域间IO估计时产生令人满意的结果。如果可以使用一个完整的区域间IO数据，则该方法可以通过Chenery-Mosse模型到Isard类型的转换技术来再现与给定数据完全一致的估计。从这些结果可以看出，我们的方法提供了一个理论上一致的和实际可行的SCGE模型。然而，由于时间限制和求解大规模非线性方程组的需要，我们无法利用本研究中创建的区域间IO数据进行SCGE分析。由于这不是一个本质问题，而仅仅是一个技术问题，我们对此持乐观态度。较少