2019冠状病毒病时期的失业:基于流量的实时失业预测方法

A. Sahin, Murat Tasci, Jin Yan
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引用次数: 7

摘要

本文提出了一种基于流量的实时失业率预测方法,并表明该方法在2020年春季COVID-19衰退开始时,在预测失业率峰值及其在一年内的快速下降方面表现得更好。它提出了基于这种方法的2021年的另一种情景分析,并认为失业率可能会在2021年底降至5.4%。该方法的预测能力来自于实时数据与流方法的结合使用。
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Unemployment in the Time of Covid-19: A Flow-Based Approach to Real-Time Unemployment Projections
This paper presents a flow-based methodology for real-time unemployment rate projections and shows that this approach performed considerably better at the onset of the COVID-19 recession in the spring 2020 in predicting the peak unemployment rate as well as its rapid decline over the year. It presents an alternative scenario analysis for 2021 based on this methodology and argues that the unemployment rate is likely to decline to 5.4 percent by the end of 2021. The predictive power of the methodology comes from its combined use of real-time data with the flow approach.
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