This study forecasts a particular type of economic uncertainty (inflation uncertainty) in the United States and Euro Area over 1997–2017. By using monthly data, we compute inflation uncertainty based on three models: symmetric and asymmetric generalized autoregressive conditional heteroscedasticity models and a stochastic volatility model. While the first two provide symmetric and asymmetric measures of inflation uncertainty, respectively, the third measure offers greater flexibility when measuring uncertainty. The analysis of the out-of-sample forecasts for inflation uncertainty shows the superiority of the stochastic volatility model for forecasting the dynamics of inflation uncertainty in both the short (1 year) and medium (4 years) terms. This finding is particularly interesting, as it allows researchers to better estimate the main inflation cost, namely inflation uncertainty, as well as its effect on the real economy.
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EDC Paris Business Sch, OCRE Lab, Paris, France
Univ Tunis, High Inst Management, ISGT, LR13ESOI GEF2A, Tunis 1002, TunisiaEDC Paris Business Sch, OCRE Lab, Paris, France
Ftiti, Zied
Jawadi, Fredj
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Univ Evry, 2 Rue Facteur Cheval, F-91025 Evry, FranceEDC Paris Business Sch, OCRE Lab, Paris, France
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Univ Leicester, Leicester, England
Bank Greece, Athens, Greece
Pretoria Univ, Pretoria, South AfricaUniv Leicester, Leicester, England
Hall, Stephen G.
Tavlas, George S.
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Bank Greece, Athens, Greece
Stanford Univ, Hoover Inst, Stanford, CA USA
Bank Greece, 21 E Venizelos Ave, Athens 10250, GreeceUniv Leicester, Leicester, England
Tavlas, George S.
Wang, Yongli
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Univ Birmingham, Birmingham, EnglandUniv Leicester, Leicester, England