"克强指数"是英国著名政经杂志《经济学人》推出的用于评估中国经济增长的指标,由工业耗电量、铁路货运量和贷款发放量三个指标组成。本文基于1978-2011年的时间序列数据,在建立向量自回归(V A R)模型、向量误差修正(V E C)..."克强指数"是英国著名政经杂志《经济学人》推出的用于评估中国经济增长的指标,由工业耗电量、铁路货运量和贷款发放量三个指标组成。本文基于1978-2011年的时间序列数据,在建立向量自回归(V A R)模型、向量误差修正(V E C)模型的基础上,综合运用Johansen协整检验和Granger因果检验等计量经济学方法,对我国"克强指数"各变量与经济增长之间的互动关系进行研究。实证结果表明工业用电量、铁路货运量、银行贷款与经济增长之间存在长期均衡关系和短期调整机制。展开更多
In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock c...In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.展开更多
文摘"克强指数"是英国著名政经杂志《经济学人》推出的用于评估中国经济增长的指标,由工业耗电量、铁路货运量和贷款发放量三个指标组成。本文基于1978-2011年的时间序列数据,在建立向量自回归(V A R)模型、向量误差修正(V E C)模型的基础上,综合运用Johansen协整检验和Granger因果检验等计量经济学方法,对我国"克强指数"各变量与经济增长之间的互动关系进行研究。实证结果表明工业用电量、铁路货运量、银行贷款与经济增长之间存在长期均衡关系和短期调整机制。
文摘In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.
