In the process of the Government keeps pushing ahead with deleveraging, the leverage ratios of SOEs and nonSOEs has diverged, and the decrease of the leverage ratios of SOEs is significantly lower than that of nonSOEs.This paper constructs a DSGE model that integrates vertical structure of industries and monetary policy uncertainty to discuss the causes of structural divergence of leverage ratios between SOEs and nonSOEs in China.The main conclusions are as follows:（1） The shock of monetary policy uncertainty is an important reason for the structural divergence of the leverage ratios of enterprises;（2） The special vertical structure of industries in China and government implicit guarantee can amplify the impact of monetary policy uncertainty shock on the leverage ratios of enterprises, and when decreasing the linkage degree of vertical structure of industries or increasing the proportion of government implicit guarantee, the further squeeze out the financing of nonSOEs, resulting in the more significant reduction of leverage ratios of nonSOEs;（3） Expectation management can restrain the impact of monetary policy uncertainty on enterprises’ leverage ratios by reasonably guiding the expectations of enterprises on monetary policy. On the basis of clarifying the causes of structural divergence of leverage ratios of Chinese enterprises, this paper puts forward the following policy suggestions: In the process of deleveraging, we should pay attention to strengthening the budget constraint of SOEs; Maintain the continuity of monetary policy; Correcting the ownership preference of banks’ credit allocation; Increasing the communication of Central Bank, so as to manage expectations of enterprises on the Monetary Policy.

Based on the reality of incomplete information and credit rent-seeking from the credit market in China, this paper constructs a DSGE model that integrates formal banks, rent-seeking banks and the search and matching process of credit market. This paper analyzes the influence mechanism of credit market distortion on credit matching and total factor productivity through numerical simulation, and the mechanism and effectiveness of relevant policy regulation are simulated. The results show that: (1) Both the noise signal for return on capital of enterprises and credit rent-seeking behavior distort the mechanism for the price allocation in the credit market, resulting in the misallocation of credit funding. While the inefficient state-owned enterprises expand production, they squeeze out the credit funding of the productive non-state-owned enterprises, which has a negative impact on the total factor productivity. (2) The regulatory mechanism corrects the error of bank’s expectation of credit, while guides banks to allocate credit funding according to the optimal decision path of profit maximization. The regulatory mechanism effectively promotes the rational allocation of credit funding, which improves the total factor productivity; Financial anti-corruption not only restrains the credit rent-seeking behavior, but also aggravates the financing difficulty of non-state-owned enterprises, which is difficult to promote the total factor productivity. (3) Gradually promoting the construction of credit marketization and eliminating the distortion of credit market, which could promote the allocation of credit funding from state-owned enterprises to non-state-owned enterprises. The actions realize the efficient allocation of credit funds and improve the level of total factor productivity. Based on the above research results, this paper puts forward policy recommendations from four aspects: the mechanism of improving disclosure for information in the credit market, establishing a fair competition mechanism in the credit market, establishing a regulatory mechanism for the decision-making errors of bank credit, and strengthening efforts for financial anti-corruption.