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.
Highlights
The IEA’s World Energy Outlook 2017 report states that oil, natural gas, and coal are still the world's leading energy sources by 2035, and it expects that oil demand will continue to grow at an average annual rate of 0.7%
The results show that the high-frequency micro-information contained in the GARCH-Mixed Data Sampling (MIDAS) model can improve the prediction accuracy, especially the prediction accuracy is higher in predicting the long-term fluctuation
The univariate MIDAS (m, K) model proposed by Ghysels et al [9] can directly integrate the (m) low-frequency data yt and high-frequency data xt parameterized polynomial weights W ( Ll/m ; θ ): into the following regression model by using (m) yt = α + βW ( Ll/m ; θ ) xt where, ε t ~N(0, σ2 ), yt denotes the t-th low-frequency response variable, xt denotes the t-th high-frequency explanatory variable, t denotes the time of low-frequency data, and m denotes the number of the high-frequency data of t-th period
Summary
The IEA’s World Energy Outlook 2017 report states that oil, natural gas, and coal are still the world's leading energy sources by 2035, and it expects that oil demand will continue to grow at an average annual rate of 0.7%. CPI and PPI data monthly, financial market returns daily, stock market fluctuation intraday, etc.), researchers are often confronted by the difficulty in exploring the true effect of oil price on the economy because most existing models are based on the same-frequency data In response to this problem, some scholars use alternative indicators that have the same data frequency as the explanatory variables [5]; some use the method of adding or replacing to convert high frequency data to low frequency data [6]; and others choose interpolation to process low-frequency data as high-frequency data [7]. We propose an analysis of the impact of the international oil price fluctuation on the development of various industries in China in the context of mixed-frequency data, which is the first in the literature, to the best of our knowledge. This study constructs a nonlinear least squares model with first-order lag autoregressive terms of explanatory variables that can play an important role in comparative analysis, and further enhance the superiority of the mixed-frequency data sampling model over the same-frequency data sampling model
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