dc.contributor.author
Chuliá Soler, Helena
dc.contributor.author
Garrón Vedia, Ignacio
dc.contributor.author
Uribe Gil, Jorge Mario
dc.date.issued
2022-07-12T09:56:05Z
dc.date.issued
2022-07-12T09:56:05Z
dc.identifier
https://hdl.handle.net/2445/187583
dc.description.abstract
Using a high-frequency framework, we show that the Auroba-Diebold-Scotti (ADS) daily business conditions index significantly increases the accuracy of U.S. unemployment nowcasts in real-time. This is of particular relevance in times of recession, such as the Global Financial Crisis and the Covid-19 pandemic, when the unemployment rate is prone to rise steeply. Based on our results, the ADS index presents itself as a better predictor than the financial indicators widely used by the literature and central banks, including both interest and credit spreads and the VXO.
dc.format
application/pdf
dc.publisher
Universitat de Barcelona. Facultat d'Economia i Empresa
dc.relation
Reproducció del document publicat a: http://www.ub.edu/irea/working_papers/2022/202211.pdf
dc.relation
IREA – Working Papers, 2022, IR22/11
dc.relation
[WP E-IR22/11]
dc.rights
cc-by-nc-nd, (c) Chuliá Soler et al., 2022
dc.rights
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))
dc.subject
Anàlisi de regressió
dc.subject
Mostreig (Estadística)
dc.subject
Regression analysis
dc.subject
Sampling (Statistics)
dc.title
Monitoring daily unemployment at risk
dc.type
info:eu-repo/semantics/workingPaper