基于AMF-HXA的湖南省城乡居民收入差异研究
Using AMF-HXA for Difference of Residents’ Income between Urban and Rural Areas of Hunan Province
DOI: 10.12677/SA.2020.91004, PDF,    科研立项经费支持
作者: 柏泽伟, 文欣薇, 刘 深, 李雅芝, 王 访*:湖南农业大学信息与智能科学技术学院,湖南 长沙
关键词: 仿多重分形高度互相关分析人均收入灰色预测Analogous Multifractal Height Cross-Correlation Analysis Per Capita Disposable Income Grey Forecast
摘要: 随着湖南省经济的飞速发展,我省城乡人均收入的差距越来越明显。收入的不均衡不仅体现了地区经济发展的差异,另一方面也反映了我国改革开放进程中城乡发展的不同步。利用仿多重分形高度互相关分析,以湖南省14个市州的城镇、农村的人均收入数据为研究对象,研究了人均收入的波动差异性及互相关性。其后并利用灰色预测模型对各市州城乡收入进行了预测。结果表明:发达地区的城镇间人均收入的波动差异小,而贫困地区的城镇间的波动具有较大差异;分别对于城镇和乡村,不同区域之间的互相关性显著;灰色预测模型拟合效果显著。这些结论为探寻湖南省城乡人均收入的相互影响提供了一个新视角。
Abstract: With the rapid economic development of Hunan province, the per capita disposable income gap between urban and rural areas is becoming more and more obvious. The income imbalance not only reflects the difference of regional economic development, but also reflects the asynchronous development of urban and rural areas in the process of China’s reform and opening up. By using analogous multifractal height cross-correlation analysis (AMF-HXA), we study the per capita disposable income of difference and the cross correlation of the 14 cities in Hunan province. After that, by using the grey forecasting model, we forecast the income of urban and rural areas respectively. The results show that the fluctuation of per capita disposable income is small between cities and towns in developed areas, while that is large in poor areas. There is significant cross-correlation between the different regions we divided for both urban areas and rural areas. The fitting effect of grey prediction model is remarkable. These conclusions provide a new per-spective for exploring the interaction between urban and rural per capita income in Hunan province.
文章引用:柏泽伟, 文欣薇, 刘深, 李雅芝, 王访. 基于AMF-HXA的湖南省城乡居民收入差异研究[J]. 统计学与应用, 2020, 9(1): 26-38. https://doi.org/10.12677/SA.2020.91004

参考文献

[1] 刘文, 房光婷. 珠三角、长三角、环渤海区域城乡居民收入差距研究[J]. 云南财经大学学报, 2010(1): 132-143.
[2] 景跃军, 李雪. 我过城乡居民收入区域差异分析与对策[J]. 经济与管理, 2014, 28(2): 34-38.
[3] Bahmani-Oskooee, M. and Motavallizadeh-Ardakani, A. (2018) On the Effects of Income Volatility on Income Distribution: A Symmetric Evidence from State Level Data in the US. Research in Economics, 72, 224-239. [Google Scholar] [CrossRef
[4] Yang, L.M., Kong, L.L., Shen, Y.N. and Ge, Y.Y. (2014) Analysis on Regional Difference in Relationship between Financial Development and Urban-Rural Income Gap. In: Proceedings of the Eighth International Conference on Management Science and Engineering Management, Springer, Berlin, Heidelberg, 325-335. [Google Scholar] [CrossRef
[5] Ma, X., Wang, F.R., Chen, J.D. and Zhang, Y. (2017) The Income Gap between Urban and Rural Residents in China: Since 1978. Computational Economics, 52, 1153-1174. [Google Scholar] [CrossRef
[6] 彭真善. 中国东、中、西部地区城乡收入差距比较分析[J]. 经济地理, 2009, 29(7): 1087-1091.
[7] 王少国. 我国城乡收入差距的地区类型分析[J]. 技术经济与管理研究, 2011(5): 85-88.
[8] Podobnik, B. and Stanley, H.E. (2008) Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Nonstationary Time Series. Physical Review Letters, 100, 84-102. [Google Scholar] [CrossRef
[9] Zebende, G.F. (2011) DCCA Cross-Correlation Coefficient: Quantifying Level of Cross-Correlation. Physica A, 390, 614-618. [Google Scholar] [CrossRef
[10] Zhou, W.X. (2008) Multifractal Detrended Cross-Correlation Analysis for Two Nonstationary Signals. Physical Review E, 77, Article ID: 066211. [Google Scholar] [CrossRef
[11] Kalamaras, N., Philippopoulos, K. and Deligiorgi, D. (2017) Scaling Properties of Meteorological Time Series Using Detrended Fluctuation Analysis. Perspectives on Atmospheric Sciences, 78, 545-550. [Google Scholar] [CrossRef
[12] Yin, Y. and Shang, P.J. (2015) Multiscale Multifractal Detrended Cross-Correlation Analysis of Traffic Flow. Nonlinear Dynamics, 81, 1329-1347. [Google Scholar] [CrossRef
[13] Wang, F., Liao, G.P., Li, J.H., Zou, R.B. and Shi, W. (2013) Cross-Correlation Detection and Analysis for California’s Electricity Market Based on Analogous Multifractal Analysis. Chaos, 23, Article ID: 013129. [Google Scholar] [CrossRef] [PubMed]
[14] Wang, F., Yang, Z.H. and Wang, L. (2016) Detecting and Quantifying Cross-Correlations by Analogous Multifractal Height Cross-Correlation Analysis. Physica A, 444, 954-962. [Google Scholar] [CrossRef
[15] Wang, F., Wang, L. and Chen, Y.M. (2018) Quantifying the Range of Cross-Correlated Fluctuations Using a q-L Dependent AHXA Coefficient. Physica A, 494, 454-464. [Google Scholar] [CrossRef
[16] Barabási, A.L., Szépfalusy, P. and Vicsek, T. (1991) Multifractal Spectra of Multi-Affine Functions. Physica A, 178, 17-28. [Google Scholar] [CrossRef
[17] 段杰, 张娟. 基于灰色预测的深圳文化创意产业发展对经济增长贡献研究[J]. 中国人口•资源与环境, 2014, 24(S1): 457-460.
[18] Lu, P., Cai, S., Yang, P. and Rosenbaum, M.S. (2005) Disintegration Characteristics of Weak Rocks Using the Grey Prediction Technique. Geotechnical & Geological Engineering, 23, 131-139. [Google Scholar] [CrossRef
[19] 赵楠. 经济变量间满足幂律关系的一些研究[D]: [硕士学位论文]. 武汉: 华中科技大学, 2013.
[20] 郑烨, 王春萍, 张顺翔, 李子恒. 精准扶贫提升农户满意度的作用机制研究——基于西部某省三贫困县的实证调查[J]. 软科学, 2018(11): 15-19.