I study the effects of the 2020 coronavirus outbreak in the United States and subsequent fiscal policy response in a nonlinear DSGE model. The pandemic is a shock to the utility of contact-intensive services that propagates to other sectors via general equilibrium, triggering a deep recession. I use a calibrated version of the model that matches the path of the US unemployment rate in 2020 to analyze different types of fiscal policies. I find that the pandemic shock changes the ranking of policy multipliers. Unemployment benefits are the most effective tool to stabilize income for borrowers, who are the hardest hit during a pandemic, while liquidity assistance programs are the most effective if the policy objective is to stabilize employment in the affected sector. I also study the effects of the $2.2 trillion CARES Act of 2020.

我研究了 2020 年冠状病毒在美国爆发的影响以及随后在非线性 DSGE 模型中的财政政策响应。大流行对接触密集型服务的效用造成冲击，这种冲击会通过一般均衡传播到其他部门，引发深度衰退。我使用与 2020 年美国失业率路径匹配的模型的校准版本来分析不同类型的财政政策。我发现大流行冲击改变了政策乘数的排名。失业救济金是稳定借款人收入的最有效工具，借款人在大流行期间受到的打击最严重，而如果政策目标是稳定受影响部门的就业，则流动性援助计划是最有效的。我还研究了 2.2 万亿美元的 2020 年 CARES 法案的影响。