Nowcasting unemployment insurance claims in the time of COVID-19.
Int J Forecast
; 38(2): 635-647, 2022.
Article
in English
| MEDLINE | ID: covidwho-1019088
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
/
Qualitative research
/
Randomized controlled trials
Language:
English
Journal:
Int J Forecast
Year:
2022
Document Type:
Article
Affiliation country:
J.ijforecast.2021.01.001
Similar
MEDLINE
...
LILACS
LIS