Mixed-frequency Asymmetric Measure of China's Electric Power Industry Prosperity Index

doi:10.11930/j.issn.1004-9649.202006195

Electric Power ›› 2022, Vol. 55 ›› Issue (6): 172-185.DOI: 10.11930/j.issn.1004-9649.202006195

• Technology and Economics • Previous Articles Next Articles

Mixed-frequency Asymmetric Measure of China's Electric Power Industry Prosperity Index

ZHOU Decai^1,2, CHEN Jinjin¹, LIU Bo¹

1. School of Economics and Management, Nanchang University, Nanchang 330031, China;
2. Research Center for Economic and Social Development in Central China, Nanchang 330031, China

Received:2020-06-19 Revised:2021-04-15 Online:2022-06-28 Published:2022-06-18
Supported by:
This work is supported by Jiangxi University Humanities and Social Sciences Research Project (Compilation and Application Research of Jiangxi Real-Time Time-varying Economic Prosperity Index System Based on Particle Filter, No.TJ19102), National Bureau of Statistics of China (Research on Real-Time Time-Varying Forecast of China's Macroeconomics Based on Mixed-Frequency Data, No. 2018LZ09）and National Social Science Foundation of China (Compilation and Application Research of China's Real-Time Dynamic Financial Condition Index Based on Mixed-Frequency Loss Function, No. 2018LZ09).

Abstract

Abstract: In view of the problem that the traditional co-frequency power prosperity index lacks timeliness and dynamism, a MF-MS-SW model is constructed, which can analyze both the quarterly and monthly frequencies at the same time. Leading, consistent and lagging mixed-frequency sample data consisting of 21 economic indicators are selected to construct China's mixed-frequency power prosperity index and early warning signal system. The results show that the proposed MF-MS-SW measurement model has a good description of the fluctuation characteristics of China’s power prosperity index, i.e., multiple-frequency and nonlinear characteristics, and has a high degree of consistency with China’s overall economic development status. The model can be used for early warning and forecasting. It is recommended that the energy and power sectors regularly compile and publish China's power prosperity index, reflecting the real-time changes in the power industry's economic status and promoting the optimization and adjustment of energy structure.

Key words: electric power industry, prosperity index, warning signal analysis, MS-SW model, mixed frequency model

ZHOU Decai, CHEN Jinjin, LIU Bo. Mixed-frequency Asymmetric Measure of China's Electric Power Industry Prosperity Index[J]. Electric Power, 2022, 55(6): 172-185.

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References

[1] 马精华. 地市电力景气指数研究[D]. 广州: 华南理工大学, 2010.
MA Jinghua. Research on prosperity analysis of electricity market in Foshan[D]. Guangzhou: South China University of Technology, 2010.
[2] 蒋贲. 用电市场景气分析研究[D]. 北京: 华北电力大学(北京), 2009.
JIANG Ben. Prospect analysis of electricity consumption market[D]. Beijing: North China Electric Power University, 2009.
[3] 刘畅, 高铁梅. 中国电力行业周期波动特征及电力需求影响因素分析: 基于景气分析及误差修正模型的研究[J]. 资源科学, 2011, 33(1): 169–177
LIU Chang, GAO Tiemei. Characteristics of electricity industry cycle fluctuation and influential factors of electricity demand based on business analysis and the error correction model[J]. Resources Science, 2011, 33(1): 169–177
[4] 梁亚丽. 中国电力需求周期波动特征及影响因素研究[D]. 北京: 华北电力大学(北京), 2005.
LIANG Yali. Studying for cyclic fluctuation characteristics and influencing element of electricity demand in China[D]. Beijing: North China Electric Power University, 2005.
[5] 周宏, 黄婷, 戴韧, 等. 几种灰色模型用于电力消费中期预测研究[J]. 电网技术, 2000, 24(7): 49–54
ZHOU Hong, HUANG Ting, DAI Ren, et al. Study on alternative grey models to forecast middle term electric power consumption[J]. Power System Technology, 2000, 24(7): 49–54
[6] 姜勇. 基于模糊聚类的神经网络短期负荷预测方法[J]. 电网技术, 2003, 27(2): 45–49
JIANG Yong. Short-term load forecasting using a neural network based on fuzzy clustering[J]. Power System Technology, 2003, 27(2): 45–49
[7] 谢宏, 陈志业, 牛东晓. 短期电力负荷预测的数据主成份分析[J]. 电网技术, 2000, 24(1): 43–46
XIE Hong, CHEN Zhiye, NIU Dongxiao. Principle component analysis of data processing for short\|term load forecasting[J]. Power System Technology, 2000, 24(1): 43–46
[8] 马朝, 史雷, 王莹, 等. 天津电力景气指数模型的研究与应用[J]. 电气时代, 2017(1): 88–90, 95
[9] 杨槿梅. 用电市场景气状况分析方法的研究[D]. 北京: 清华大学, 2004.
YANG Jinmei. Research on prosperity analysis of electricity market[D]. Beijing: Tsinghua University, 2004.
[10] FESTIC M, REPINA S, VOLCJAK R. Estimating coal price dynamics with the principal components method[J]. Romanian Journal of Economic Forecasting, 2010, 13(2): 188–212.
[11] ZELLOU A M, CUDDINGTON J T. Trends and super cycles in crude oil and coal prices[J]. Working Papers, 2012.
[12] 张洪潮, 王泽江, 李晓利, 等. 中国煤炭消费需求波动规律及成因分析[J]. 中国人口·资源与环境, 2014, 24(1): 94–101
ZHANG Hongchao, WANG Zejiang, LI Xiaoli, et al. The fluctuation pattern of Chinese coal consumption demand and cause analysis[J]. China Population, Resources and Environment, 2014, 24(1): 94–101
[13] 王哲. 石油价格冲击因素及石油相关产业周期波动研究[D]. 大连: 东北财经大学, 2007.
[14] FRALE C, MARCELLINO M, MAZZI G L, et al. Survey data as coincident or leading indicators[J]. Journal of Forecasting, 2010, 29(1/2): 109–131.
[15] 叶光. 基于混频数据的一致指数构建与经济波动分析[J]. 统计研究, 2015, 32(8): 17–26
YE Guang. Research on the coincident index and economic fluctuations in China with mixed-frequency data[J]. Statistical Research, 2015, 32(8): 17–26
[16] MARIANO R S, MURASAWA Y. A coincident index, common factors, and monthly real GDP[J]. Oxford Bulletin of Economics and Statistics, 2010, 72(1): 27–46.
[17] 周德才, 童飞杰, 胡琛宇. 基于混频损失函数的中国实时金融状况指数另种构建[J]. 数量经济技术经济研究, 2018, 35(12): 134–154
ZHOU Decai, TONG Feijie, HU Chenyu. Another construction of Chinese real-time financial conditions index based on mixed frequency loss function[J]. The Journal of Quantitative & Technical Economics, 2018, 35(12): 134–154
[18] 郑挺国, 王霞. 中国经济周期的混频数据测度及实时分析[J]. 经济研究, 2013, 48(6): 58–70
ZHENG Tingguo, WANG Xia. Measuring China's business cycle with mixed-frequency data and its real time analysis[J]. Economic Research Journal, 2013, 48(6): 58–70
[19] 周德才, 朱志亮, 纪应心, 等. 混频非对称金融景气指数编制及应用研究: 基于MF-MS-DFM模型的经验分析[J]. 管理评论, 2019, 31(8): 71–83
ZHOU Decai, ZHU Zhiliang, JI Yingxin, et al. Construction and application of Chinese mixed frequency asymmetric financial prosperity index—based on an empirical analysis of MF-MS-DFM model[J]. Management Review, 2019, 31(8): 71–83
[20] CAMACHO M, PEREZ QUIROS G, PONCELA P. Green shoots and double dips in the euro area: a real time measure[J]. International Journal of Forecasting, 2014, 30(3): 520–535.
[21] CAMACHO M. Mixed-frequency VAR models with Markov-switching dynamics[J]. Economics Letters, 2013, 121(3): 369–373.
[22] 周德才, 邓姝姝, 左玥. 中国金融状况指数混频编制与应用研究: 基于MS-MF-VAR模型的一个经验分析[J]. 南开经济研究, 2018(2): 148–163
ZHOU Decai, DENG Shushu, ZUO Yue. Mixed frequency construction and application of Chinese financial conditions index: based on an empirical analysis of MS-MF-VAR model[J]. Nankai Economic Studies, 2018(2): 148–163
[23] KIM C J, NELSON C R. State-space models with regime switching[M]. (s. n.): The MIT Press, 2017.
[24] 杨武, 宋盼, 解时宇. 基于季度数据的区域科技创新景气指数研究[J]. 科研管理, 2015, 36(5): 55–64
YANG Wu, SONG Pan, XIE Shiyu. A research on the regional technology innovation climate index based on the quarterly data[J]. Science Research Management, 2015, 36(5): 55–64
[25] STOCK J H, WATSON M W. New indexes of coincident and leading economic indicators[J]. NBER Macroeconomics Annual, 1989, 4: 351–394.

Mixed-frequency Asymmetric Measure of China's Electric Power Industry Prosperity Index

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