影响我国钢材需求量的因素分析
Analysis the Influence Factors of Steel Demand in China

作者: 孙小军 :云南财经大学统计与数学学院,云南 昆明;

关键词: 成品钢材需求量多元线性回归逐步回归Lasso回归Steel Demand Multiple Regression Stepwise Regression Lasso Regression

摘要:
钢材工业在国民经济中起着举足轻重的作用,它被广泛用于经济建设、国防建设、社会发展等方面。但随着我国国民经济的快速发展和产业结构的调整,我国长期粗放发展模式使钢材工业受到很大冲击,使我国钢材工业向着精细化方向发展已迫在眉睫。本文使用原油产量、原煤产量、天然气产量、水泥产量、生铁产量、发电量、全社会固定资产投资额、居民消费、政府消费等9个因素,对我国成品钢材需求量进行分析,建立一般线性回归模型、逐步回归模型和Lasso回归模型等三个回归模型,通过比较,发现Lasso回归模型的预测效果最好,并根据所给模型对今后钢材发展提出了建议。

Abstract: Steel industry plays an important role in the national economy, which is widely used in economic construction, national defense construction, and other aspects of social development, but with the development of the national economy and the adjustment of industrial structure, long-term and extensive development pattern in China made great impact on the steel industry, fine direction so that China's steel industry development is imminent. This article uses the nine factors, including crude oil output, output of raw coal, natural gas production, cement production, pig iron produc- tion, power generation, the whole society fixed assets investment, consumer and government spending, to demand for steel products in China were analyzed, and establish three models, including the general linear regression model, the stepwise regression model and three Lasso regression model regression model. By comparison, find the Lasso regression model prediction effect is best, and according to the given model for the development of steel are proposed.

文章引用: 孙小军 (2017) 影响我国钢材需求量的因素分析。 社会科学前沿, 6, 441-451. doi: 10.12677/ASS.2017.64061

参考文献

[1] 宝良, 郗维强. 我国钢材需求分析研究[J]. 内蒙古科技与经济, 2006(4): 2-5.

[2] 刘铁敏, 周伟, 王青. 依据钢铁生产过程的中国铁矿石需求预测模型[J]. 金属矿山, 2007(2): 2-4.

[3] 李凯, 代丽华, 韩爽. 应用生长曲线模型预测中国钢铁工业的峰值点[J]. 冶金经济与管理, 2005(2): 1-3.

[4] 吴文东, 吴刚, 魏一鸣. 基于相关系数的钢材需求量组合预测[J]. 中国管理科学, 2008(16): 45-49.

[5] 赵月红. 中国钢材需求量的预测研究[J]. 内蒙古科技与经济, 2008(6): 320-323.

[6] Ghosh, S. (2006) Steel Consumption and Economic Growth: Evidence from India. Resources Policy, 1, 7-11.

[7] Yu, B., Qiao, Y., Li, X., et al. (2014) Low-Carbon Transition of Iron and Steel Industry in China: Carbon Intensity, Economic Growth and Policy Intervention. Journal of Environmental Sciences, 16, 10-16.

[8] Crompton, P. (2009) Future Trends in Japanese Steel Consumption. Resources Policy, 22, 103-104.

[9] http://www.stats.gov.cn/tjsj/ndsj/

[10] 何晓群, 刘文卿. 应用回归分析(第二版)[M]. 北京: 中国人民大学出社, 2007: 18-92.

[11] 吕海燕, 李海燕, 李武林. 基于逐步回归分析的粮食产量因素研究[J]. 河南科学, 2013(12): 1-3.

[12] 吴喜之. 复杂数据统计方法——基于R的应用[M]. 北京: 中国人民大学出版社, 2015.

分享
Top