Forecasting Inflation Uncertainty in the G7 Countries
Stelios Bekiros and
Bernd Wilfling ()
No 7118, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster
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
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Journal Article: Forecasting Inflation Uncertainty in the G7 Countries (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:cqe:wpaper:7118
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