Combination Prediction of Income Gap between Urban and Rural Residents in China Based on IOWA Operator

Yuwan Huang *

School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, China.

*Author to whom correspondence should be addressed.


Aims: Based on the income data of urban and rural residents in China from 1998 to 2021, the income gap variables of urban and rural residents were constructed, and the combination prediction method was used to predict the income gap between urban and rural residents in China.

Methodology: Grey prediction model GM (1,1), Holt-winter seasonless exponential smoothing model and autoregressive moving average ARIMA model were used to construct an order weighted arithmetic mean combination model induced by IOWA with the minimum sum of error squares. Then, by building new weights, three individual forecasting models and combination forecasting models are used to forecast the income gap between urban and rural residents in the next five years.

Results: The results show that the accuracy of the combined prediction model is significantly better than that of the single prediction model.

Conclusion: It can be known that the income gap between urban and rural residents will widen further in the future, with an average growth rate of 4.55%.

Keywords: IOWA operator, combination prediction, Grey prediction model, ARIMA, HM model

How to Cite

Huang , Y. (2023). Combination Prediction of Income Gap between Urban and Rural Residents in China Based on IOWA Operator. Asian Journal of Probability and Statistics, 22(4), 31–40.


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China Population Census Yearbook; 2020 National Bureau of Statistics. China Population Census Yearbook; 2020.

Yanqun Zhang, Haiyuan Wan. Determinants and trend forecast of income gap between urban and rural residents [J]. Journal of Quantitative & Technical Economics. 2019;36(03): 59-75. (In Chinese)

Xuhai Zheng, Tao Zhang. Analysis and Prediction of urban-rural Income Gap based on ARIMA Model: A Case study of Yunnan Province [J]. Rural Economics and Science and Technology, 2018, 29(15): 139-141. (In Chinese)

Gu ZY. Forecast of urban-rural income gap in Jiangsu Province based on ARMA-GM combination model.] Journal of Changshu University of Technology, 2012,26(08): 18-21. (In Chinese)

Yifei Zhang, Sheng Cheng, Jue Wang et al. Tradeoff between global and extreme Deviations: An improved combinatorial predictive optimization method [J/OL]. System Engineering Theory and Practice: 1-15 [2023-05-18]. (In Chinese)

Panfeng Li, Zujun Ma, Hao Sun. Prediction of blood supply and demand based on SARIMA Combination prediction model [J/OL]. Industrial engineering and management: 1-18 [2023-05-18]. (In Chinese)

Xiaowo Tang, Yongkai Ma, Yong Zeng, et al. Research on Modern Portfolio Forecasting and Portfolio Investment Decision Method and Its Application [M]. Beijing: Science Press; 2003. (In Chinese)

Huayou Chen, Xiang Li, Lei Jin, Mengjie Yao. Prediction method of interval combination based on Correlation Coefficient and IOWA Operator [J]. Statistics and Decision. 2012;(06):83-86. (In Chinese)

Huayou Chen, Zhaohan Sheng. A new combinational prediction method based on IOWGA Operator [J]. Journal of Management Engineering. 2005(04):39-42. (In Chinese)

Ye Yang, Hongjun Yuan, Lingyun Hu. Based on IGOWMA operator interval type variable weight coefficient of combination forecast model [J]. Journal of Yanbian University (Natural Science Edition). 2023,49 (01):8-15. (In Chinese)

Zhou TN. Combination prediction of urban-rural income gap in Zhejiang Province based on IOWA operator. Journal of Changchun Institute of Technology (Natural Science Edition). 2019;22(01):111-117-123. (In Chinese)

Wenqian You, Kejun Zhuang. Prediction of grain yield combination based on IOWA operator [J]. Journal of Chongqing Technology and Business University (Natural Science Edition). 2020;37(05):80-87. (In Chinese)

Lijin Guo, Jiahao Zhang. Prediction of wheat yield based on IOWA operator combined grey neural network [J]. Food and Oils. 202;35(10):26-30. (In Chinese)