Local government debt risk measurement and early warning structural system building — Research Based on KMV and TOPSIS Models

In this paper, by using the Statistical analysis Software SPSS20, 30 and Cities of our country’s local government structural debt risk has been carried on the empirical analysis, using the entropy weight method to determine the specific weight of each index, reduced the subjective factors of influence on the result of the index weight distribution, expectations of local government debt defaults and the situation of the comprehensive evaluation index of local government debt. By comparing the size of the threshold value, to determine the risk of local government debt, the results show that in 7 provinces and cities in China are in the state of no warning of local debt risk, in 10 provinces and cities are in the state of mild warning. Means and Cities are in the State of Moderate warning, In other words, it means it’s time to generate the risk of local government debt. In the State of high warning, most means and cities need to take scientific measures to generate the risk of local government debt