Based On Gray Theory Forecasting Analysis To The Income Of Urban Residents

Since Reform and Opening up, China's economy has developed rapidly and has scored remarkable achievement in the world. Instead of the pre-reform highly equalitarianism under dual economic structure, the income-distribution structure of Chinese residents has turned into current distribution pattern which gives priority to efficiency with due consideration to fairness and combines remuneration according to work with remuneration according to factors of production put in.In recent years, China's economy has grown rapidly and the income level of residents has continuously risen. At the same time, the income differential between workers is crescent. The gap between the rich and the poor has become increasingly apparent. At present, the Gini coefficient to describe the gap between the rich and the poor in our country has exceeded the International Warning Line, which is up to 0.469. Therefore, doing research to the income in urban and rural residents of China and to find a suitable prediction model is of strong practical significance.The paper expounds China's current income distribution and its problems and analyses the income prediction of in urban and rural residents. The paper applies the gray correlation analysis method and gray GM(1,1) model of gray theory to the income prediction of in urban and rural residents. Chapter 2 explains the gray correlation indicators of the income of urban residents and relevant factors and points out the impact to the income of urban residents by the indicators. Chapter 3 chooses relevant data in 2008 China Statistical Yearbook, makes prediction and analysis to the income of urban residents by traditional GM(1,1) and does research to the model error and precision. Chapter 4 makes research to predictive precision of traditional GM(1,1) model and puts forward three improving aspects and further improves an aspect. Through estimation and analysis to some parameters, it is obtained that the use of improved GM(1,1) model to predict and analyze the same data sets will make better effect.