We investigate the channels through which financial inclusion can be achieved in China. The Partial Least Squares approach to Structural Equation Modelling is used to analyse the relationships among financial literacy, Internet usage, digital financial products usage, and financial inclusion. We show that Internet usage has no direct impact on financial inclusion. Rather, the direct impact comes from the level of financial literacy and the use of digital financial products, which are advanced by popularity of the Internet. Internet usage and digital financial products usage play a multiple mediation role between financial literacy and financial inclusion. We conclude that to achieve the goal of advancing financial inclusion, Chinese policymakers should improve the consumers’ financial literacy and promote the use of digital financial products.

Measures of national financial inclusion mostly focus on traditional financial services and ignore the role of digital finance. We incorporate several digital elements of financial inclusion and construct a comprehensive index of digital financial inclusion for 101 countries in 2017. The spatial distribution of the index shows a strong geographical aggregation and a clustered pattern in national income groups. Regions with higher-income countries tend to have better digital financial inclusion. However, digital technology has helped several low-income countries to improve their financial inclusion. But this improvement does not spillover to other low-income countries in the region.

This paper uses the short-run restrictions implied by a simple aggregate demand-aggregate supply model as an aid in identifying structural shocks. Combined with the Blanchard—Quah restriction, it allows estimation of the slope of the aggregate supply curve, the variances of structural demand and supply shocks, and the extent to which structural demand and supply shocks are correlated. This paper finds that demand and supply shocks are highly correlated and that demand shocks possibly can account for as much as 82% of the long-run forecast error variance of real U.S. GDP.

We investigate the asymmetric risk–return relationship in a time-varying beta CAPM. A state space model is established and estimated by the Adaptive Least Squares with Kalman foundations proposed by McCulloch. Using S&P 500 daily data from 1987:11–2003:12, we find a positive risk–return relationship in the up market (positive market excess returns) and a negative relationship in the down market (negative market excess returns). This supports the argument of Pettengill et al., who use a constant beta model. However, our model outperforms theirs by eliminating the unexplained returns and improving the accuracy of the estimated risk price.