基于弹性网回归的云南省财政收入影响因素分析
Analysis on the Influencing Factors of Yunnan Province’s Fiscal Revenue Based on Elastic Net

作者: 于秀君 :云南师范大学数学学院,云南 昆明;

关键词: 岭回归LASSO回归弹性网回归财政收入Ridge Regression LASSO Regression Elastic Net Fiscal Revenue

摘要: 经济学变量相互之间往往存在很强的相关性,这使得模型变得复杂。在本文中,首先,对原始数据进行多重共线性诊断;然后,基于弹性网回归,并借助交叉验证方法确定参数和各参数估计值对云南省财政收入相关数据进行建模分析;最后,将弹性网回归与岭回归以及LASSO回归估计结果进行分析比较。结果表明弹性网回归优于岭回归与LASSO回归,同时得出云南省财政收入受税收收入、地区生产总值、社会消费品零售总额、在岗职工工资总额、社会就业人数、第一产业产值、全社会固定资产投资以及全省旅游业总收入的影响。

Abstract: Economic variables often have strong correlations with each other, complicating the model. In this paper, we conduct a multiple collinearity test on the original data at first; then, Yunnan Province’s fiscal revenue related data are modeling and analyzed by cross validation method. Finally, the results of Elastic net regression and Ridge regression and LASSO regression estimations are analyzed and compared. At the same time, it is concluded that the Yunnan Province’s fiscal revenue is affected by tax revenue, regional gross domestic product, total retail sales of consumer goods, total wages of employed workers, number of social employment, output value of primary industry, investment in fixed assets of the whole society and total income of tourism province.

文章引用: 于秀君 (2021) 基于弹性网回归的云南省财政收入影响因素分析。 统计学与应用, 10, 415-419. doi: 10.12677/SA.2021.103041

参考文献

[1] 朱德云, 李萌. 经济欠发达地区财政收入增长影响因素研究——基于山东菏泽的样本分析[J]. 财贸经济, 2012(7): 21-28.

[2] 毛琴, 李明江, 刘彦. 基于逐步回归法的国家财政收入数据回归模型分析[J]. 电子技术与软件工程, 2013(19): 227-228.

[3] 刘心竹, 李昊, 刘青青, 等. 我国地方财政收入影响因素的实证分析[J]. 中国集体经济, 2012(9): 84-85.

[4] 邓洁. 我国财政收入影响因素的实证分析[J]. 金卡工程, 2009, 13(9): 180-181.

[5] Zou, H. and Hastie, T. (2005) Addendum: “Regularization and Variable Selection via the Elastic Net”. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 301-320.
https://doi.org/10.1111/j.1467-9868.2005.00503.x

[6] Hoerl, A.E. and Kennard, R.W. (1970) Ridge Regression: Applications to Nonorthogonal Problems. Technometrics, 12, 69-82.
https://doi.org/10.1080/00401706.1970.10488635

[7] Hoerl, A.E. and Kennard, R.W. (1970) Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12, 55-67.
https://doi.org/10.1080/00401706.1970.10488634

[8] Tibshirani, R. (2011) Regression Shrinkage and Selection via the Lasso: A Retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73, 267-288.
https://doi.org/10.1111/j.1467-9868.2011.00771.x

[9] Zou, H. (2006) The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association, 101, 1418- 1429.

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