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

Abstract

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%.