 Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shockTony Klein Journal of Economic Behavior & Organization, 2022, vol. 194, issue C, 264-286 Abstract: We analyze an unbalanced panel of 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. Keywords: COVID19; Employment; Forecasting; Machine learning; Survey data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:194:y:2022:i:c:p:264-286 DOI: 10.1016/j.jebo.2021.11.028 Access Statistics for this article Journal of Economic Behavior & Organization is currently edited by Houser, D. and Puzzello, D. More articles in Journal of Economic Behavior & Organization from Elsevier |
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