The US-China Trade War and Global Value Chains (Job Market Paper) [ click here for the latest version ] [slides]
This paper studies the heterogeneous impact of the US-China trade war in the presence of global value chains. By building a two-stage, multi-country, multi-sector general equilibrium model, this paper discusses how tariffs on imports affect domestic producers through within and across industry linkage. The model shows that tariffs on imports of Chinese upstream intermediate goods negatively affect US downstream exports, output and employment. The effects are strong in US industries that rely on targeted Chinese intermediate goods. In addition, this paper quantifies the impacts of the two rounds of the trade war by comparing tariffs on intermediate goods and consumption goods. This paper estimates that the trade war contributes to US CPI by 0.09% in the first round and 0.22% in the second round. Finally, this paper studies the welfare effects of the trade war. This paper estimates that in terms of aggregate real income, the trade war costs China $35.2 billion, or 0.29% of GDP, and costs the US $15.6 billion, or 0.08% of GDP.
The Collapse of the International Trade During the Great Recession
During the Great Recession in 2008-2009, the U.S. experienced a significant collapse in international trade. Compared with previous downturns, the trade reduction in this period is exceptional in terms of its magnitude and rapidness. Distinct from other recessions in the 20th century, the recession in 2008-2009 is sparked by a drastic credit crunch in the financial crisis. To explain the uniqueness of 2008-2009 trade collapse, this paper builds an International Real Business Cycle model with financial friction. This paper demonstrates a negative correlation between financial friction and trade volume, and compares a case where financial crisis only occurs in the U.S. with a case where financial crisis occurs both in the U.S. and its trading partners.
The Forecast of Macroeconomic Indicators: High-Dimensional Prediction with Forecast Combination, with Gang Cheng, Yuhong Yang
This paper forecasts macroeconomic indicators using high-dimensional data and forecast combination. Compared with Stock and Watson 2003, this paper improves in two aspects: First, when building individual models this paper replaces the traditional AIC stepwise selection with “Lasso” selection. Second, in the model combination this paper applies a new method named “AFTER”. Unlike the simple combination, which sets constant weights to individual forecasts, “AFTER” updates the weights of individual models based on their past performances (Zou and Yang 2004). Using the same data as Stock and Watson 2003 , this paper finds that “AFTER” outperforms the simple combination when forecasting CPI inflation and employment rate, but has no significant improvement when forecasting real GDP growth rate. Besides, this paper finds replacing AIC selection by Lasso selection does not significantly improve forecasting performance.