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Forecasting Inflation Uncertainty in the United States and Euro Area

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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.

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Notes

  1. Further, economic uncertainty, which is always unobserved (Charles et al. 2018), has always been challenging to measure and several proxies have been used: the VIX (Bloom et al. 2012), conditional variance models (Scotti 2012; Rossi and Sekhposyan 2017), the economic policy index (Baker et al. 2015), and perceived uncertainty from consumer surveys (Leduc and Sill 2013).

  2. It reached 1.5 and 2.96% in 2010 and 2011, respectively. It then changed to about 1.74% in 2012, 1.5% in 2013, 0.76% in 2014, 0.73% in 2015, and 2.07% in 2016.

  3. It decreased to 1.64 and 0.92% in 2008 and 2009, respectively before reaching 2.23, 2.75, and 2.22% in 2010, 2011, and 2012.

  4. p and q denote the lag order of the autoregressive and moving average parts, respectively. They are specified by using the information criteria and autocorrelation functions.

  5. The lags for a GARCH model might be specified by using information criteria, too. However, a GARCH(1,1) provides a suitable specification with which to capture the main volatility properties.

  6. Other methods of modelling stochastic volatility include Gaussian error models and heavy tails and serial dependence; however, the t-distribution is more appropriate (Chan 2013).

  7. For more details on the MDM statistic, see Harvey et al. (1997).

  8. The GARCH model is estimated by using the quasi-maximum likelihood technique of Bollerslev and Wooldridge (1992).

  9. To save space, we do not report the estimation results of the GARCH and GJR-GARCH specifications, but they are available upon request.

  10. For the medium term forecasting, the period of models estimation is from June 1997 to January 2013 for the case of US and from February 1997 to January 2013. The forecasting period is from February 2013 to January 2017.

  11. For the short-term forecast, the period of the model estimation runs from June 1997 to January 2016 for the United States and from February 1997 to January 2016 for the Euro Area. The forecasting period is therefore from February 2016 to January 2017.

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Authors and Affiliations

  1. EDC Paris Business School, OCRE-Lab, Paris, France

    Zied Ftiti

  2. High Institute of Management, ISGT, LR13ES01-GEF2A, University of Tunis, Le Bardo, Bouchoucha, 1002, Tunis, Tunisia

    Zied Ftiti

  3. University of Evry, 2, rue Facteur Cheval, 91025, Evry, France

    Fredj Jawadi

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  1. Zied Ftiti
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Correspondence to Zied Ftiti.

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Ftiti, Z., Jawadi, F. Forecasting Inflation Uncertainty in the United States and Euro Area. Comput Econ 54, 455–476 (2019). https://doi.org/10.1007/s10614-018-9794-9

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