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Forecasting time series with long memory and level shifts
Author(s) -
Hyung Namwon,
Franses Philip Hans
Publication year - 2005
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.937
Subject(s) - computer science , series (stratigraphy) , volatility (finance) , econometrics , representation (politics) , feature (linguistics) , time series , point estimation , interval (graph theory) , point (geometry) , machine learning , statistics , mathematics , paleontology , linguistics , philosophy , geometry , combinatorics , politics , political science , law , biology
It is well known that some economic time series can be described by models which allow for either long memory or for occasional level shifts. In this paper we propose to examine the relative merits of these models by introducing a new model, which jointly captures the two features. We discuss representation and estimation. Using simulations, we demonstrate its forecasting ability, relative to the one‐feature models, both in terms of point forecasts and interval forecasts. We illustrate the model for daily S&P500 volatility. Copyright © 2005 John Wiley & Sons, Ltd.