Twenty nineteen (2019) marked another year of lethargic growth in the Chinese economy amidst escalated internal and external complexities. Internally, the country’s macroeconomic landscape was overcast continuously by fallen consumption growth, plunged growth in manufacturing investment, rapid accumulation of household debt, risen income inequality, and the overhang of local government debt. The nation’s external conditions did not fare any better, with drastically declined growth in imports and exports, continued trade tensions with the US, and weakened external demand. Based on the IAR-CMM model, which takes account of both cyclical and secular factors, the baseline real GDP growth rate is projected to be 6.0% in 2020 (5.9% using more reliable rather than the official data), with a downside risk. Alternative scenario analyses and policy simulations are conducted, in addition to the benchmark forecast, to reflect the influences of various internal and external uncertainties. The findings emanated from these analyses lead us to stress the importance and urgency of deepening reform to achieve competitive neutrality for China’s transformation into a phase with sustainable and high-quality development.

The ever-normal granary system was an official granary management system in ancient China. Throughout its existence, the system functioned as a major means of adjusting the price of grain and provided disaster relief. Few studies on the system touches upon the relationship between grain price fluctuation and the development of the grain market, or the ever-normal granary system and its related economic school of thoughts. Starting with the development of the grain market and the relationship between the price of grain and grain reserves, and through a systematic review of the debate on the ever-normal granary system and grain prices among high-level officials of the Qing government in the 13th year of Emperor Qianlong’s reign (1748), this paper analyzes the historical process and reasons for the change in thoughts on the ever-normal granary system and discusses the historical path of how economic phenomenon gave rise to the clash of economic thoughts that influenced the evolution of this economic institution.

Business cycle dynamics are determined by relatively large volatilities in output, consumption, and investment, which leads to cyclical fluctuations in interest rates. Using the Markov switching model, we model the nominal interest rate movements to explain the volatility regime shifts in a set of selected emerging Asian economies. The estimated results provide significant evidence of regime-dependent means, variances, and probabilities in both stable and volatile regimes in selected countries, confirming the existence of two distinct regimes in nominal interest rate movements. In addition, the smoothed probability results of switching autoregressive model show that the model is capable of capturing the two regimes for the corresponding nominal interest rate behaviors. Besides, the results reveal that the stables regimes have higher durations than the volatile regimes. This study also shows the advantage of Markov switching models over conventional regression models, allowing the identification of different regimes for the cyclical behavior of interest rates.

A dramatic surge in online peer-to-peer (P2P) lending emerged in China, where (under conditions of credit deficiency) it took only three years for the size of the P2P lending market in China to reach four times that of the United States and ten times that of the United Kingdom. The literature indicates that ownership structure is an important factor that influences P2P lending firms’ performance, while research on the underlying mechanisms remain insufficient. This study analyzes the data of P2P lending companies between June 2016 and March 2017. The results demonstrate that although ownership structure has minimal direct effect on the turnover volume and number of lenders and borrowers, it moderates the effects of firm age, interest rate, and loan term on firm performance. These results enrich the property theory and shed light on how P2P lending firms with different ownership structures could succeed when there is institutional deficiency.

China has been the world's largest automobile producer since 2009, but it still lags behind other countries in terms of productivity. Based on the National Bureau of Statistics of China (NBSC) firm-level data and the improved approach proposed by Ackerberg et al. (2015), this paper investigates the contribution of total factor productivity (TFP) growth to the Chinese automobile industry and evaluates the impact of firm entry and exit on TFP growth. The empirical results show that the TFP of the Chinese automobile industry grows at 10.7% per year. Joint venture and foreign-owned firms have a significantly higher TFP growth rate than others. Large-scale firms have a higher TFP growth rate than do small-scale firms, but the latter have caught up after 2004. Moreover, the entry of new firms and exit of old firms significantly improve the aggregate TFP growth rate.

Deviations from the efficient market hypothesis allow us to benefit from risk premium in financial markets. We propose a three-pronged (R, σ, H) theory to generalize the (R, σ) model and present the formulation of a three-pronged (R, σ, H) model and its Pareto-optimal solution. We define the local-optimal weights (wR, wσ,wH) that construct the triangle of the quasi-optimal investing subspace and further define the centroid or incenter of the triangle as the optimal investing weights that optimize the mean return, risk premium, and volatility risk. By numerically investigating the Chinese stock market, we demonstrate the validity of this formulation method. The proposed theory provides investors of different styles (conservative or aggressive) an efficient way to design portfolios in financial markets to maximize the mean return while minimizing the volatility risk.