Refined analysis and prediction of natural gas consumption in China
Abstract:: In view of the abrupt and phased features of natural gas consumption, this paper attempts to predict natural gas consumption in China with a refined forecasting approach. First, we establish a Markov switching (MS) model to identify the phase characteristics after eliminating change point...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2019-06-01
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| Series: | Journal of Management Science and Engineering |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096232019300782 |
| _version_ | 1818864530890948608 |
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| author | Ting Liang Jian Chai Yue-Jun Zhang Zhe George Zhang |
| author_facet | Ting Liang Jian Chai Yue-Jun Zhang Zhe George Zhang |
| author_sort | Ting Liang |
| collection | DOAJ |
| description | Abstract:: In view of the abrupt and phased features of natural gas consumption, this paper attempts to predict natural gas consumption in China with a refined forecasting approach. First, we establish a Markov switching (MS) model to identify the phase characteristics after eliminating change points in the natural gas consumption sequence, using the product partition model (PPM). The results show that there are “rapid growth” and “slow growth” regimes in the development process of natural gas consumption in China. Second, the Bayesian model average (BMA) method is employed to determine the core determinants of natural gas consumption under sub-regimes, and it is determined that there are significant differences in the influencing factors under different regimes and periods. Third, this paper establishes the BMA model of the “rapid growth” regime after predicting the state of future natural gas consumption in China. We find that, compared to some other models, the BMA model that fully recognizes the regime without considering change points has the best predictive performance. Finally, the results of static and dynamic scenario analyses show that natural gas consumption continues to rise in 2019 and has obvious seasonal characteristics, while possible ultra-rapid growth of consumption in the future provides a new requirement for the supply of natural gas. Keywords: Natural gas consumption, PPM model, Markov switching model, BMA model, Scenario analysis |
| first_indexed | 2024-12-19T10:33:08Z |
| format | Article |
| id | doaj.art-ee31f8bf87a74931a02fc1e61233c126 |
| institution | Directory Open Access Journal |
| issn | 2096-2320 |
| language | English |
| last_indexed | 2024-12-19T10:33:08Z |
| publishDate | 2019-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Management Science and Engineering |
| spelling | doaj.art-ee31f8bf87a74931a02fc1e61233c1262022-12-21T20:25:43ZengKeAi Communications Co., Ltd.Journal of Management Science and Engineering2096-23202019-06-014291104Refined analysis and prediction of natural gas consumption in ChinaTing Liang0Jian Chai1Yue-Jun Zhang2Zhe George Zhang3Business School, Hunan University, Changsha, 410082, ChinaSchool of Economics & Management, Xidian University, Xi'an, 710126, China; College of Business & Economics, Western Washington University, Bellingham, 98225, USA; Corresponding author. 266 Xinglong Section of Xifeng Road, Xi'an, 710126, China.Business School, Hunan University, Changsha, 410082, China; Corresponding author. Lushan Road (S), Yuelu District, Changsha, 410082, China.College of Business & Economics, Western Washington University, Bellingham, 98225, USAAbstract:: In view of the abrupt and phased features of natural gas consumption, this paper attempts to predict natural gas consumption in China with a refined forecasting approach. First, we establish a Markov switching (MS) model to identify the phase characteristics after eliminating change points in the natural gas consumption sequence, using the product partition model (PPM). The results show that there are “rapid growth” and “slow growth” regimes in the development process of natural gas consumption in China. Second, the Bayesian model average (BMA) method is employed to determine the core determinants of natural gas consumption under sub-regimes, and it is determined that there are significant differences in the influencing factors under different regimes and periods. Third, this paper establishes the BMA model of the “rapid growth” regime after predicting the state of future natural gas consumption in China. We find that, compared to some other models, the BMA model that fully recognizes the regime without considering change points has the best predictive performance. Finally, the results of static and dynamic scenario analyses show that natural gas consumption continues to rise in 2019 and has obvious seasonal characteristics, while possible ultra-rapid growth of consumption in the future provides a new requirement for the supply of natural gas. Keywords: Natural gas consumption, PPM model, Markov switching model, BMA model, Scenario analysishttp://www.sciencedirect.com/science/article/pii/S2096232019300782 |
| spellingShingle | Ting Liang Jian Chai Yue-Jun Zhang Zhe George Zhang Refined analysis and prediction of natural gas consumption in China Journal of Management Science and Engineering |
| title | Refined analysis and prediction of natural gas consumption in China |
| title_full | Refined analysis and prediction of natural gas consumption in China |
| title_fullStr | Refined analysis and prediction of natural gas consumption in China |
| title_full_unstemmed | Refined analysis and prediction of natural gas consumption in China |
| title_short | Refined analysis and prediction of natural gas consumption in China |
| title_sort | refined analysis and prediction of natural gas consumption in china |
| url | http://www.sciencedirect.com/science/article/pii/S2096232019300782 |
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