Forecasting Inflation Uncertainty in the United States and Euro Area

Zied Ftiti () and Fredj Jawadi

Computational Economics, 2019, vol. 54, issue 1, No 18, 455-476

Abstract: Abstract 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.

Keywords: Inflation uncertainty; Forecasts; GARCH model; Asymmetry; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: C2 C53 E31 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10614-018-9794-9

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