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Forecasting Inflation Uncertainty in the G7 Countries

Mawuli Segnon, Stelios Bekiros and Bernd Wilfling ()

No 7118, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

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: C22 E31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
Date: 2018-03
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