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Energies 2018, 11(6), 1372; https://doi.org/10.3390/en11061372

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

1
School of Economics and Management, Xidian University, Xi’an 710126, China
2
Nanjing University of Information Science & Technology, Jiangsu Talents Research Base, Nanjing 210044, China
3
Department of Management Sciences, City University of Hong Kong, Hong Kong 999077, China
4
Department of Decision Sciences, College of Business and Economics, Western Washington University, Bellingham, WA 98225, USA
*
Author to whom correspondence should be addressed.
Received: 1 May 2018 / Revised: 21 May 2018 / Accepted: 25 May 2018 / Published: 28 May 2018
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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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. View Full-Text
Keywords: MIDAS; international oil prices; macroeconomic variables MIDAS; international oil prices; macroeconomic variables
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Chai, J.; Cao, P.; Zhou, X.; Lai, K.K.; Chen, X.; Su, S.S. The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data. Energies 2018, 11, 1372.

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