This page has only limited features, please log in for full access.
In finance and economics, predictive regression models are widely used. It is known that the limit distributions of their least squares estimators are nonstandard, and depend on the properties of the predictors. In this paper, we consider the unified confidence region construction of a predictive regression model by using empirical likelihood. It turns out that the resulting statistic has an asymptotical chi-squared distribution regardless of the predictor being stationary or non-stationary. Simulations are also conducted to illustrate its finite sample performance.
Xiaohui Liu; Yuzi Liu; Fucai Lu. Empirical likelihood-based unified confidence region for a predictive regression model. Communications in Statistics - Simulation and Computation 2019, 1 -18.
AMA StyleXiaohui Liu, Yuzi Liu, Fucai Lu. Empirical likelihood-based unified confidence region for a predictive regression model. Communications in Statistics - Simulation and Computation. 2019; ():1-18.
Chicago/Turabian StyleXiaohui Liu; Yuzi Liu; Fucai Lu. 2019. "Empirical likelihood-based unified confidence region for a predictive regression model." Communications in Statistics - Simulation and Computation , no. : 1-18.
The industrial sector is a major contributor to resource consumption and environmental pollution in China. The energy-intensive industrial development and energy structure are dominated by coal, which has produced an enormous amount of industrial pollutants in China, and put great pressure on the ecological environment. Hence, improving the performance of industrial green development (PIGD) has become an urgent task of utmost importance. This study applies a global non-radial directional distance function to estimate the PIGD for Jiangxi Province during 2003–2015, and provides targeted policy suggestions. The empirical results show a rising trend in the PIGD in Jiangxi Province. At the city level, Nanchang and Fuzhou performed considerably better than other cities in regards to their PIGD. However, the poor environmental performance caused by the excessive discharge of industrial pollutants has also hindered its PIGD. Most cities in Jiangxi Province failed to efficiently use resources, especially energy and labor, in industrial production. The results of the influencing factor analysis show that the performance of industrial green development in Jiangxi could be improved through increasing per capita GDP, decreasing the share of coal consumption in the total industrial energy consumption, and decreasing the share of industrial GDP in the total GDP. Furthermore, a more efficient use of environmental management investment funds and timely transfer of the surplus industrial labor are needed.
Wei Wang; Hualin Xie; Fucai Lu; Xinmin Zhang. Measuring the Performance of Industrial Green Development Using a Non-Radial Directional Distance Function Approach: A Case Study of Jiangxi Province in China. Sustainability 2017, 9, 1757 .
AMA StyleWei Wang, Hualin Xie, Fucai Lu, Xinmin Zhang. Measuring the Performance of Industrial Green Development Using a Non-Radial Directional Distance Function Approach: A Case Study of Jiangxi Province in China. Sustainability. 2017; 9 (10):1757.
Chicago/Turabian StyleWei Wang; Hualin Xie; Fucai Lu; Xinmin Zhang. 2017. "Measuring the Performance of Industrial Green Development Using a Non-Radial Directional Distance Function Approach: A Case Study of Jiangxi Province in China." Sustainability 9, no. 10: 1757.