Date Published: May 10, 2019
Publisher: Public Library of Science
Author(s): Jingyu Chen, Faqi Jin, Guangda Ouyang, Jian Ouyang, Fenghua Wen, Stefan Cristian Gherghina.
This paper combines a Granger causality test and a VAR model to investigate the relationships among oil price shocks, global economic policy uncertainty (GEPU), and China’s industrial economic growth. Based on monthly data from 2000 to 2017, we reveal that GEPU and world oil prices jointly Granger cause China’s industrial economic growth; world oil prices have a positive effect on China’s industrial economic growth, while GEPU has a negative effect. Further analyses investigate the asymmetry effect of oil prices and find that the negative component shows a more significant impact on China’s industrial economic growth. The results are robust to different oil price and EPU proxies.
Crude oil, the black blood of industry, plays an important role in economic growth [1–4] and inspires the great interest of researchers [5–9]. Given the importance of crude oil, there is also attention paid to what would happen to the economy when its price changes. The classical theory about the relationship between oil and economic growth is based on supply-side theory. As an indispensable raw material in production, oil price determines the oil’s cost. Producers would adjust their production plan to achieve the maximum profit, which would affect output and in turn affect the macroeconomy (see, e.g., [10–13]).
The VAR model is adapted to examine the effect of oil price shocks and global economic policy uncertainty on China’s industrial economic growth. Oil price changes can influence the demand for industrial output, and economic policy uncertainty can change the views of investors. Moreover, oil price shocks and economic policy uncertainty co-affect economic growth by influencing the expected risks to the environment and the expected relationship between supply and demand.
When the model crossing a long period of time, we should pay close attention to the Structural stability. Considering the breakpoint is unknown, referring to Ewing and Malik , we conduct the iterated cumulative sum of squares (ICSS) developed by Inclan and Tiao . This method is algorithm based on IT statistic for testing multiple breaks in the unconditional variance to detect structural breaks in the unconditional variance of oil price returns. The conventional significance is set at 5% level to test for multiple breaks of oil prices. From Fig 3 we can see that there is no breakpoint when oil price series are monthly data, because compared to weekly data and daily data, monthly data drop a lot of information.
Different from the previous studies of the impact of oil price shocks on China’s economic growth, this paper systematically integrates the world oil price, global economic policy uncertainty and economic growth. Using monthly data on oil prices and economic growth, we construct a VAR model, empirically analyze the impact of international oil prices and economic policy uncertainty on China’s industrial economic growth, and reach the following main conclusions.