This paper examines how different uncertainty measures affect the unemployment level, inflow, and outflow in the U.S. across all states of the business cycle. We employ linear and nonlinear causality-in-quantile tests to capture a complete picture of the effect of uncertainty on U.S. unemployment. To verify whether there are any common effects across different uncertainty measures, we use monthly data on four uncertainty measures and on U.S. unemployment from January 1997 to August 2018. Our results corroborate the general predictions from a search and matching framework of how uncertainty affects unemployment and its flows. Fluctuations in uncertainty generate increases (upper-quantile changes) in the unemployment level and in the inflow. Conversely, shocks to uncertainty have a negative impact on U.S. unemployment outflow. Therefore, the effect of uncertainty is asymmetric depending on the states (quantiles) of U.S. unemployment and on the adopted unemployment measure. Our findings suggest state-contingent policies to stabilize the unemployment level when large uncertainty shocks occur.

中文翻译：

---

美国不确定性和失业流动之间的不对称动态

本文研究了不同的不确定性措施如何影响美国经济周期所有状态的失业率、流入和流出。我们采用线性和非线性分位数因果关系检验来全面了解不确定性对美国失业的影响。为了验证不同不确定性度量之间是否存在任何共同影响，我们使用了 1997 年 1 月至 2018 年 8 月四种不确定性度量和美国失业率的月度数据。我们的结果证实了不确定性如何影响失业率和匹配框架的一般预测。它的流动。不确定性的波动会导致失业率和流入量增加（上分位数变化）。相反，对不确定性的冲击对美国失业外流有负面影响。所以，不确定性的影响是不对称的，这取决于美国失业的州（分位数）和所采用的失业措施。我们的研究结果表明，当发生巨大的不确定性冲击时，国家或有政策可以稳定失业水平。