Zheng-Xin Wang

The quantile regression technique is introduced into the Lotka–Volterra ecosystem analysis framework. The quantile grey Lotka–Volterra model is established to reveal the dynamic trade relationship between China and the United States. An optimisation model is constructed to solve optimum quantile parameters. The empirical results show that the quantile grey Lotka–Volterra model shows higher fitting accuracy and reveals the trade relationships at different quantiles based on quarterly data on China–US trade from 1999 to 2019. The long-term China–US trade relationship presents a prominent predator–prey relationship because exports from China to the US inhibited China’s imports from the United States. Moreover, we divide samples into five stages according to four key events, China’s accession to the WTO, the 2008 global financial crisis, the weak global economic recovery in 2015, and the 2018 China–US trade war, recognising various characteristics at different stages.

Improved measurement methods for relative index of financial inclusion (IFI) based on Euclidean distance and Mahalanobis distance (hereinafter referred to as Ed-R-IFI and Md-R-IFI) are proposed. The levels of financial inclusion of 55 countries in 2004 are evaluated by using the traditional and improved measurement methods for the IFI. The results indicate that, owing to consideration of the existence of correlations among the various dimensions, the Md-R-IFI method reduces the overall level of financial inclusion and keeps the IFI within a small range of fluctuation compared with the traditional measurement method and the Ed-R-IFI method. The results are more in line with the actual IFIs in various countries, and the method has better applicability and practicality in the measurement of the IFI.