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Forecasting Inflation Uncertainty in the G7 Countries

1
Department of Economics (CQE), Westfälische Wilhelms-Universität Münster, Am Stadtgraben 9, 48143 Münster, Germany
2
Department of Accounting and Finance, Athens University of Economics and Business, Trias 2, GR 11362 Athens, Greece
3
Department of Economics, European University Institute, Via delle Fontanelle 18, I-50014 Florence, Italy
*
Author to whom correspondence should be addressed.
Econometrics 2018, 6(2), 23; https://doi.org/10.3390/econometrics6020023
Received: 16 February 2018 / Revised: 22 February 2018 / Accepted: 16 April 2018 / Published: 27 April 2018
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. View Full-Text
Keywords: inflation uncertainty; smooth transition; multifractal processes; GARCH processes inflation uncertainty; smooth transition; multifractal processes; GARCH processes
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Segnon, M.; Bekiros, S.; Wilfling, B. Forecasting Inflation Uncertainty in the G7 Countries. Econometrics 2018, 6, 23.

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