It is necessary to analyze the relationship between financial inclusion and circumstances-monetary policy and economic fundamentals, which has a practical reference value for policy makers. This paper studies the impact of the circumstances on financial inclusion factors by using a vector autoregressive method. Empirical results show that monetary policy has a short-term positive impact on financial inclusion factors, while the economic fundamental has the opposite, which means that the positive monetary policy promote the development of financial inclusion in the short term and the sudden change of the economic situation will make it harder. Based on the data of the World Bank and the situation of China, we make an analysis and comparison of the empirical results, and draw two implications: first, the sustainable development of financial inclusion needs a suitable circumstance; second, the appropriate coordination and mutual facilitation of economic fundamentals and finance is conducive to the sustainable development of financial inclusion.

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The dynamic relations among national economic growth, economic disparity, and financial disparity in China are examined. Specifically, the focus is on whether economic disparity or financial disparity affects national economic growth. As measures of economic and financial disparity across regions and provinces, the Williamson coefficient of disparity is employed using both regional data (eastern, central, and western) and provincial data (from 31 provinces). Overall, it is found that both provincial financial disparity and, to a lesser degree, economic disparity have a negative effect on national economic growth. In addition, financial disparity appears to be exogenous, suggesting that financial disparity is not influenced by either economic disparity or national economic growth.* Lee (corresponding author):

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Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper's method is robust and performs well compared to random walk.