Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/187583
Title: Monitoring daily unemployment at risk
Author: Chuliá Soler, Helena
Garrón, Ignacio
Uribe Gil, Jorge Mario
Keywords: Anàlisi de regressió
Mostreig (Estadística)
Unemployment
Regression analysis
Sampling (Statistics)
Atur
Issue Date: 2022
Publisher: Universitat de Barcelona. Facultat d'Economia i Empresa
Series/Report no: [WP E-IR22/11]
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.
Note: Reproducció del document publicat a: http://www.ub.edu/irea/working_papers/2022/202211.pdf
It is part of: IREA – Working Papers, 2022, IR22/11
URI: http://hdl.handle.net/2445/187583
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

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