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Nowcasting Unemployment Insurance Claims in the Time of COVID-19

William Larson () and Tara Sinclair

No 2020-004, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting

Abstract: Near term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. Models including Google Trends are outperformed by alternative models in nearly all periods. Our results suggest that in times of structural change there is a bias-variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.

Keywords: panel forecasting; time series forecasting; forecast evaluation; structural breaks; Google Trends (search for similar items in EconPapers)
JEL-codes: C53 E24 E27 J64 R23 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2020-06, Revised 2020-08
New Economics Papers: this item is included in nep-ets, nep-for, nep-lab, nep-mac and nep-ore
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https://www2.gwu.edu/~forcpgm/2020-004.pdf First version, 2020 (application/pdf)

Related works:
Journal Article: Nowcasting unemployment insurance claims in the time of COVID-19 (2022) Downloads
Working Paper: Nowcasting unemployment insurance claims in the time of COVID-19 (2020) Downloads
Working Paper: Nowcasting Unemployment Insurance Claims in the Time of COVID-19 (2020) Downloads
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