Nowcasting unemployment insurance claims in the time of COVID-19

William Larson and Tara Sinclair

International Journal of Forecasting, 2022, vol. 38, issue 2, 635-647

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. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. 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)
Date: 2022
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Citations: View citations in EconPapers (9)

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Working Paper: Nowcasting unemployment insurance claims in the time of COVID-19 (2020)
Working Paper: Nowcasting Unemployment Insurance Claims in the Time of COVID-19 (2020)
Working Paper: Nowcasting Unemployment Insurance Claims in the Time of COVID-19 (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:2:p:635-647

DOI: 10.1016/j.ijforecast.2021.01.001

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