Forecasting Inflation Uncertainty in the G7 Countries

Mawuli Segnon, Stelios Bekiros and Bernd Wilfling
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Mawuli Segnon: Department of Economics (CQE), Westfälische Wilhelms-Universität Münster, Am Stadtgraben 9, 48143 Münster, Germany

Econometrics, 2018, vol. 6, issue 2, 1-25

Abstract: There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - MSM ( k ) ] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA ( p , d , q ) - GARCH -type models in forecasting inflation uncertainty.

Keywords: inflation uncertainty; smooth transition; multifractal processes; GARCH processes (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Forecasting Inflation Uncertainty in the G7 Countries (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:6:y:2018:i:2:p:23-:d:143630

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