The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data

Jian Chai, Puju Cao, Xiaoyang Zhou, Kin Keung Lai, Xiaofeng Chen and Siping (Sue) Su
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Jian Chai: School of Economics and Management, Xidian University, Xi’an 710126, China
Puju Cao: School of Economics and Management, Xidian University, Xi’an 710126, China
Xiaoyang Zhou: School of Economics and Management, Xidian University, Xi’an 710126, China
Kin Keung Lai: Department of Management Sciences, City University of Hong Kong, Hong Kong 999077, China
Xiaofeng Chen: Department of Decision Sciences, College of Business and Economics, Western Washington University, Bellingham, WA 98225, USA
Siping (Sue) Su: Department of Decision Sciences, College of Business and Economics, Western Washington University, Bellingham, WA 98225, USA

Energies, 2018, vol. 11, issue 6, 1-14

Abstract: Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world’s largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China’s various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS( m , K , h )-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China’s seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China’s financial industry.

Keywords: MIDAS; international oil prices; macroeconomic variables (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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