Forecasting Inflation Uncertainty in the G7 Countries
Abstract
There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth TransitionAutoRegressive 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