美国代写:大宗商品价格波动和GDP的增长

13 11月 美国代写:大宗商品价格波动和GDP的增长

美国代写:大宗商品价格波动和GDP的增长

一些学术的学者证明有一个负面能量+商品价格之间的关系和国内生产总值(GDP)。过去十年的能源效率政策,倾向去绿色世界各地减少石油消耗。因此,调查可能影响当前的石油危机在短期内整个国家GDP增长已经选择了这项研究。体积的产品构成一个国家的商品贸易用于确定脆弱性的大宗商品价格的波动。全国的关键挑战是应对两个可能抵消力量。在短期内,贸易冲击的积极作用总是抬起GDP,和实际的问题是衡量对GDP的影响。

美国代写:大宗商品价格波动和GDP的增长

为了分析数据,线性回归模式由马克和奥尔森(1994)已经为这研究因为范式提供了很好的选择强调GDP增长之间的二元关系和商品价格。此外,它建议设立许多滞后通常,石油危机没有任何直接的影响。回归的数据已经收集到一些发展中国家和发达国家的统计数据。

数据分析提高可接受性的研究,提高研究的价值。对于这个研究,数据已经收集了来自不同国家的统计数据。数据收集后,线性回归模型被应用到分析数据,因为它重视识别变量之间的关系:大宗商品价格波动和GDP的增长

美国代写:大宗商品价格波动和GDP的增长

Several academic scholars prove that there is a negative relationship between the energy plus commodity price and Gross Domestic product (GDP). The last decade’s energy efficiency policies and tendency to go greener across the world have reduced the oil consumption. Therefore, the investigation of probable impacts of current oil shocks in short run GDP growth across the countries has been chosen for the research . The volume along with the product composition of a nation’s commodity trade uses to determine the country’s vulnerability to the volatility of the commodity price. The key challenge for the nation is to deal with two probably offsetting forces. During the short run, the positive effects of trade shocks will always lift up the GDP, and the practical issue is to measure the impact on the GDP .

美国代写:大宗商品价格波动和GDP的增长

In order to analyze the data, the linear regression paradigm by Mark and Olsen (1994) has been chosen for this research study because paradigm provides great emphasis on the bivariate correlation between the GDP growth and commodity price. Additionally, it recommends setting up many lags as usually, an oil shock does not have any immediate effect. The data for the regression has been collected from the statistics of several developing and developed country.

The data analyses improve the acceptability of the research study and improve the value of the research. For this particular research, the data has been collected from the statistics of different countries. After data collection, the linear regression model has been applied to analyze the data as it put great emphasis on identifying the relationship between the variable: commodity price volatility and GDP growth.