This study investigates the roles of bank and trade credits in a supply chain with a capital‐constrained retailer facing demand uncertainty. We evaluate the retailer's optimal order quantity and the creditors' optimal credit limits and interest rates in two scenarios. In the single‐credit scenario, we find the retailer prefers trade credit, if the trade credit market is more competitive than the bank credit market; otherwise, the retailer's preference of a specific credit type depends on the risk levels that the retailer would divert trade credit and bank credit to other risky investments. In the dual‐credit scenario, if the bank credit market is more competitive than the trade credit market, the retailer first borrows bank credit prior to trade credit, but then switches to borrowing trade credit prior to bank credit as the retailer's internal capital declines. In contrast, if the trade credit market is more competitive, the retailer borrows only trade credit. We further analytically prove that the two credits are complementary if the retailer's internal capital is substantially low but become substitutable as the internal capital grows, and then empirically validate this prediction based on a panel of 674 firms in China over the period 2001–2007.

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The downturn in the world economy following the global banking crisis has left the Chinese economy relatively unscathed. This paper develops a model of the Chinese economy using a DSGE framework with a banking sector to shed light on this episode. It differs from other applications in the use of indirect inference procedure to test the fitted model. The model finds that the main shocks hitting China in the crisis were international and that domestic banking shocks were unimportant. However, directed bank lending and direct government spending was used to supplement monetary policy to aggressively offset shocks to demand. The model finds that government expenditure feedback reduces the frequency of a business cycle crisis but that any feedback effect on investment creates excess capacity and instability in output.

The development of information technology has facilitated the use of the intelligent classroom model supported by information technology to improve the college students’ comprehensive quality and ability. However, the existing models are too sophisticated to be applied to the actual teaching process, and ignore the individualized teaching characteristics of students. Therefore, an intelligent classroom model with adaptive learning resource recommendation was proposed. First, the entire teaching process was divided into three stages which were used to combine teachers’ teaching and students’ learning. Then the key problems of the learning resources recommendation system was studied and a learning resource recommendation based on TR-LDA (Teaching Resources-Latent Dirichlet Allocation) was proposed and how to be achieved. Finally, the proposed intelligent classroom model was verified in practical teaching. Results show that the intelligent classroom model with adaptive learning resources recommendation can help to improve students’ learning efficiency. The relevant conclusions can be used as a reference for exploring the use of information technology to improve the quality of undergraduate professional course teaching.

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