%0 Journal Article
%T Forecasting Inflation Uncertainty in the G7 Countries
%A Bernd Wilfling
%A Mawuli Segnon
%A Stelios Bekiros
%J -
%D 2018
%R https://doi.org/10.3390/econometrics6020023
%X 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. View Full-Tex
%K inflation uncertainty
%K smooth transition
%K multifractal processes
%K GARCH processes
%U https://www.mdpi.com/2225-1146/6/2/23