Institute of Economic Research, Seoul National University
Abstract
We introduce a time series model that captures both long
memory and conditional heteroskedasticity and assess its ability to
describe the US inflation data. Specifically, the model allows for
long memory in the conditional mean formulation and uses a
normal mixture GARCH process to characterize conditional
heteroskedasticity. We find that the proposed model yields a good
description of the salient features, including skewness and
heteroskedasticity, of the US inflation data. Further, the performance
of the proposed model compares quite favorably with, for example,
ARMA and ARFIMA models with GARCH errors characterized by
normal, symmetric and skewed Student-t distributions
