摘要
现有的矩阵更新、平衡等调整方法多存在两种缺陷:一是度量新旧矩阵间差异的函数形式不对称,并非严格"距离"概念;二是要求矩阵元素非负导致使用范围受限。为改进上述不足,本文提出了包括Jensen-Shannon Divergence(JSD)在内的若干基于对称距离优化的新方法,并统一进行保号、误差妥协等扩展以方便实际运用。在此基础上利用中国及其他28个国家的数据,对比分析多种矩阵调整方法的实际效果,结果发现:第Ⅰ类JSD方法表现最突出且相对稳健,值得代替现在使用较广泛的RAS或交叉熵方法。
Matrix adjustment methods are widely used in economics, but there are still some defects in most existing methods. Firstly, the "distance" measure between the initial and estimated matrices is not symmetric;secondly, the initial matrix is assumed non-negative, however sometimes empirically inconsistent with the fact. This paper proposes various new optimization methods based on symmetric distance measure, with sign-preservation and tolerance extensions. On this basis, updating the IO table / balancing SAM as the empirical comparison with 29 countries' data, results show that the Jensen-Shannon divergence I method outperforms the others in most cases, so we suggest this new rather than RAS or CE for adjusting economic matrices in practice. (JSD- I ) approach
出处
《数量经济技术经济研究》
CSSCI
北大核心
2013年第2期111-125,共15页
Journal of Quantitative & Technological Economics
基金
国家自然科学基金"基于PGIT的财政政策选择:一个D-CGE的中国模型"(71073059)和"财政均等化政策与区域经济发展--一个中国CGE模型"(71173083)
教育部人文社会科学基金"基于CGE框架的公共支出结构分析"(09YJA790079)
中央高校基本科研专项自主创新研究基金(2011QN106)项目的资助
关键词
矩阵调整
KL散度
JS散度
RAS
Matrix Adjustment
Kullback-Leibler Divergence
Jensen-ShannonDivergence
RAS