Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/271252 
Authors: 
Year of Publication: 
2021
Series/Report no.: 
QMS Research Paper No. 2021/07
Publisher: 
Queen's University Belfast, Queen's Management School, Belfast
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: 
G12
G17
J11
G12
G17
J11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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