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

Tony Klein

No 2021/07, QBS Working Paper Series from Queen's University Belfast, Queen's Business School

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

JEL-codes: G12 G17 J11 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:qmsrps:202107

DOI: 10.2139/ssrn.3929635

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