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Cointegration rank switching model: an application to forecasting interest rates
Author(s) -
Fukuda Kosei
Publication year - 2011
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.1191
Subject(s) - cointegration , autoregressive model , rank (graph theory) , econometrics , monte carlo method , series (stratigraphy) , goodness of fit , interest rate , computer science , term (time) , mathematics , statistics , economics , finance , paleontology , physics , combinatorics , quantum mechanics , biology
This paper proposes a new forecasting method in which the cointegration rank switches at unknown times. In this method, time series observations are divided into several segments, and a cointegrated vector autoregressive model is fitted to each segment. The goodness of fit of the global model, consisting of local models with different cointegration ranks, is evaluated using the information criterion (IC). The division that minimizes the IC defines the best model. The results of an empirical application to the US term structure of interest rates and a Monte Carlo simulation suggest the efficacy as well as the limitations of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.