Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock

Tony Klein
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引用次数: 2

Abstract

We analyze an unbalanced panel monthly predictions of nonfarm payroll (NFP) changes between January 2008 and December 2020 sourced from Bloomberg. Unsurprisingly, we find that prediction quality varies across economists and we reject the hypothesis of equal predictive ability. In an error decomposition, we find evidence of significantly biased forecasts. Participation rate in the survey is affecting this bias. We find that survey participants under-predict job losses in times of market turmoil while also under-predicting the recovery thereafter, especially during the COVID19 labor shock. For prediction of NFP changes, autoregressive models are outperformed by a deep learning long short-term memory network. However, the consensus forecast yields better forecasts than model-based approaches and are further improved by combining the forecasts of the best performing economists. The COVID19 labor shock is shown to have adverse effects on the prediction performance of economists. However, not all economists are affected equally.
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同意或不同意?对2008年至2020年美国非农就业人数变化的预测以及covid - 19劳动力冲击的影响
我们分析了2008年1月至2020年12月间来自彭博社的非农就业人数(NFP)月度预测。不出所料,我们发现经济学家的预测质量各不相同,我们拒绝预测能力相等的假设。在误差分解中,我们发现预测存在显著偏差的证据。调查的参与率影响了这种偏见。我们发现,调查参与者低估了市场动荡时期的失业人数,同时也低估了市场动荡之后的复苏,尤其是在2019冠状病毒病劳动力冲击期间。对于NFP变化的预测,深度学习长短期记忆网络优于自回归模型。然而,共识预测比基于模型的方法产生更好的预测,并通过结合表现最好的经济学家的预测进一步改进。新冠肺炎疫情对经济学家的预测效果产生了不利影响。然而,并非所有经济学家都受到同样的影响。
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