摘要
本文采用1994-2016年46个国家的季度数据,构建全球向量自回归模型(GVAR)实证分析中国和其他10个地区的国际贸易冲击和对外投资冲击对彼此经济增长的影响。广义脉冲响应分析结果表明,各区域的国际贸易冲击和对外投资冲击对彼此经济增长的影响都表现出显著的异质性;中国国际贸易冲击和对外投资冲击都对“一带一路”区域和非“一带一路”区域的经济增长分别产生了促进作用和抑制作用,但“一带一路”和非“一带一路”区域的国际贸易冲击和对外投资7中击并未对中国的经济增长产生明显的组间差异性。而广义预测方差分解发现,中国的国际贸易冲击和对外投资冲击对本国经济增长的贡献率显著大于其他地区,且中国对外投资冲击比国际贸易冲击对本地经济的贡献率更大。因此,中国提升国际贸易力度和扩大对外开放程度有助于中国和“一带一路”沿线国家的经济发展。
This paper builds a global vector autoregressive (GVAR) model to empirically analyze the impact of international trade shocks and foreign investment shocks of China and other 10 regions on each other's economic growth with the quarterly data of 46 countries from 1994 to 2016. The results of generalized impulse response analysis show that significant heterogeneity is existed in the impact of international trade shocks and foreign investment shocks of various regions on each other's economic growth. The shocks of China's international trade and the shocks of foreign investment have both promoted and inhibited the economic growth of the "Belt and Road (B&R)" Region and the "non-Belt and Road(non B&:R)" Region, respectively. However, international trade shocks and foreign investment shocks of B-zR and non B-R regions don't produce any significant intra-group differences to China' s economic growth. The generalized prediction variance decomposition shows that the shocks of China' s international trade and foreign investment on its own economic growth is significantly greater than that of other regions, and the contribution rate of China' s foreign investment shocks on its economy is even greater than its international trade. Therefore, enhancing international trade and expanding opening up are contributed to the economic development of China and the countries along B&R.
作者
黄旭东
石蓉荣
HUANG Xu-dong1,2 ,SHI Rong-rong2(1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China; 2. School of Mathematics and Statistics, Anhui Normal University, Anhui Wuhu 241002, Chin)
出处
《数理统计与管理》
CSSCI
北大核心
2018年第3期492-508,共17页
Journal of Applied Statistics and Management
基金
国家自然科学基金(71601003)
全国统计科学研究重点项目(2015LZ54)
安徽省自然科学基金面上项目(1708085MG173)
安徽省高校自然科学重点基金项目(KJ2016A278)资助课题