This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the em...This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the employment data from China Statistic Yearbook for the years from 1982 to 2003. Co-integration test showed that 1% increase in export value and import value of services created respectively 0.205% and 0.068 7% more job opportunities in the service sector. Both export and import of services impacted positively on employment in service industry, and export did more than import. However, in the short run, the impacts of services export and import on employment in service industry were both very small, though positive; and the impacts of employment in service industry on both export and import of services were very big, but not stable. Granger causality test indicated that employment in service industry was a Granger cause of services export. The findings highlight the importance of facilitating services import and reducing import barriers, and suggest that the competitiveness of China's labor- intensive services trade can be exploited to boost services export and help employment in service sector, and that the structure of services trade should be optimized by shifting from labor-intensive to knowledge-and technology-intensive services thus to enhance China's competitiveness of services export.展开更多
By applying co-integration analysis,the Granger causality test and an error correction model,the dependency between the energy consumption and the gross domestic product of China was examined.In a further step an anal...By applying co-integration analysis,the Granger causality test and an error correction model,the dependency between the energy consumption and the gross domestic product of China was examined.In a further step an analysis was done to establish a correlation between the economic growth of different industries and China's energy consumption.An evidence-based study showed that a co-integration relationship exists between the gross energy consumption and the GDP of China and that the two variables possess bi-directional causality.The energy consumption for the secondary industry has a markedly stimulative effect to the economic growth.This paper also uses an error correction model(ECM)to explain the short-term behavior of energy demands.展开更多
How to achieve the objective of reducing CO2 emissions has been an academic focus in China recently. The factors influencing CO2 emissions are the vital issue to accomplish the arduous target. Firstly, three influenti...How to achieve the objective of reducing CO2 emissions has been an academic focus in China recently. The factors influencing CO2 emissions are the vital issue to accomplish the arduous target. Firstly, three influential factors, the energy consumption, the proportion of tertiary industry in gross domestic product （GDP）, and the degree of dependence on foreign trade, are carefully selected, since all of them have closer grey relation with China＇s COz emissions compared with others when the grey relational analysis （GRA） method is applied. The study highlights co-integration relation of these four variables using the co-integration analysis method. And then a long-term co-integration equation and a short-term error correction model of China＇s CO2 emissions are devel- oped. Finally, the comparison is exerted between the forecast value and the actual value of China＇s CO2 emissions based on error correction model. The results and the relevant statistics tests show that the pro- posed model has better explanation capability and credibility.展开更多
It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencin...It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017-2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017-2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.展开更多
文摘This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the employment data from China Statistic Yearbook for the years from 1982 to 2003. Co-integration test showed that 1% increase in export value and import value of services created respectively 0.205% and 0.068 7% more job opportunities in the service sector. Both export and import of services impacted positively on employment in service industry, and export did more than import. However, in the short run, the impacts of services export and import on employment in service industry were both very small, though positive; and the impacts of employment in service industry on both export and import of services were very big, but not stable. Granger causality test indicated that employment in service industry was a Granger cause of services export. The findings highlight the importance of facilitating services import and reducing import barriers, and suggest that the competitiveness of China's labor- intensive services trade can be exploited to boost services export and help employment in service sector, and that the structure of services trade should be optimized by shifting from labor-intensive to knowledge-and technology-intensive services thus to enhance China's competitiveness of services export.
基金Projects TSFZLXKF2006-3 supported by the China Lixin Risk Management Research Institute Foundation of Shanghai Municipal Education Commission90210035 by the National Natural Science Foundation of China
文摘By applying co-integration analysis,the Granger causality test and an error correction model,the dependency between the energy consumption and the gross domestic product of China was examined.In a further step an analysis was done to establish a correlation between the economic growth of different industries and China's energy consumption.An evidence-based study showed that a co-integration relationship exists between the gross energy consumption and the GDP of China and that the two variables possess bi-directional causality.The energy consumption for the secondary industry has a markedly stimulative effect to the economic growth.This paper also uses an error correction model(ECM)to explain the short-term behavior of energy demands.
基金Supported by the National Natural Science Foundation of China(41101569)the China Postdoctoral Science Foundation Funded Project(2011M500965)+5 种基金the Jiangsu Funds of Social Science(11EYC023)the Doctoral Discipline New Teachers Fund(20110095120002)the Jiangsu Postdoctoral Science Foundation Funded Project(1102088C)the Fundamental Research Funds for the Central Universities(JGJ110763)the Talent Introduction Funds of China University of Mining and Technologythe Sail Plan Funds for Young Teachers of China University of Mining and Technology~~
文摘How to achieve the objective of reducing CO2 emissions has been an academic focus in China recently. The factors influencing CO2 emissions are the vital issue to accomplish the arduous target. Firstly, three influential factors, the energy consumption, the proportion of tertiary industry in gross domestic product （GDP）, and the degree of dependence on foreign trade, are carefully selected, since all of them have closer grey relation with China＇s COz emissions compared with others when the grey relational analysis （GRA） method is applied. The study highlights co-integration relation of these four variables using the co-integration analysis method. And then a long-term co-integration equation and a short-term error correction model of China＇s CO2 emissions are devel- oped. Finally, the comparison is exerted between the forecast value and the actual value of China＇s CO2 emissions based on error correction model. The results and the relevant statistics tests show that the pro- posed model has better explanation capability and credibility.
基金supported by the National Science Foundation of China(NSFC No.41271551/71201157)the National Key Research and Development Program(2016YFA0602700)
文摘It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017-2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017-2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.
