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Detection of regime switches between stationary and nonstationary processes and economic forecasting
Author(s) -
Fukuda Kosei
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.941
Subject(s) - autoregressive model , series (stratigraphy) , econometrics , computer science , division (mathematics) , goodness of fit , time series , economics , mathematics , machine learning , paleontology , arithmetic , biology
It often occurs that no model may be exactly right, and that different portions of the data may favour different models. The purpose of this paper is to propose a new procedure for the detection of regime switches between stationary and nonstationary processes in economic time series and to show its usefulness in economic forecasting. In the proposed procedure, time series observations are divided into several segments, and a stationary or nonstationary autoregressive model is fitted to each segment. The goodness of fit of the global model composed of these local models is evaluated using the corresponding information criterion, and the division which minimizes the information criterion defines the best model. Simulation and forecasting results show the efficacy and limitations of the proposed procedure. Copyright © 2005 John Wiley & Sons, Ltd.