Lia Ghencea, AliceRamírez Arimaha, EstebanWeber Motilla, Michael2024-10-222024-10-222024http://hdl.handle.net/10230/68309Treball de Fi de Grau en Economia. Curs 2023-2024Tutor: Christian BrownleesThis study fills an important gap in the economic risk literature by extending the growth-at-risk (GaR) framework to the unemployment rate. We examine unemployment-at-risk (UaR) in seven OECD countries over the period 1983–2023 with GARCH and quantile regression type models. Results show that the GARCH(1,1)-ARMA(1,1) model provides the most accurate (conditional) volatility forecasts, and that the conditional volatilities have a particularly strong effect on the upper quantiles of the unemployment rates. When we perform the same analysis across countries, we find that UaR forecasts are country-dependent, being stationary or slightly increasing for the United States and Australia, and higher and more volatile for Portugal and Ireland. The results are important for policymakers interested in smoothing the labor market adjustments in recessions. This innovative use of the GaR framework to the country level for unemployment rates provides useful instruments for economic risk management and policy making.application/pdfengThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International LicenseTreball de fi de grau – Curs 2023-2024info:eu-repo/semantics/bachelorThesisTime series forecastinginfo:eu-repo/semantics/openAccess