Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?

Abstract

Due to the expensive infrastructures and the difficulties in storage, the supply and demand for natural gas in China has experienced periodic fluctuations and geographical imbalance. The primary purpose of this study is to provide satisfying probability density forecasts using mixed-frequency dynamic factors to alleviate the imbalance risks between the supply and the demand. To this end, this paper develops a novel hybrid model named EEMD-MIDAS-SVR, based on ensemble empirical mode decomposition (EEMD) procedure, mixed data sampling (MIDAS) framework and support vector machine regression (SVR). The empirical results reveal that the mixed-frequency dynamic factors could provide powerful predictive information for natural gas demand. By the model confidence set (MCS) test, we identify the most predictive dynamic factors: gas industry index, electricity industry index, Daqing crude oil prices, Qinhuangdao steam coal prices, West Texas Intermediate crude oil prices, and Australia steam coal prices. They could improve forecasting accuracy by about 70% than benchmark models, particularly considering the nonlinearity of natural gas demand contributes to 21% roughly. Based on the MCS test, we provide the non-parametric bootstrap probability density forecasts for natural gas demand, which performs well in capturing the uncertainty of natural gas demand. These findings have several policy implications and practical value for natural gas pricing and carbon neutrality achieving in emerging markets.