Identification of TAR models using recursive estimation
AbstractThis paper proposes an automatic procedure to identify threshold autoregressive models and specify the values of thresholds. The proposed procedure is based on the time-varying estimation of the parameters using an arranged autoregression. The proposed method not only allows for the automatic identification of the thresholds, but also has a superior identification performance than the competitors. The performance of the proposed procedure is illustrated using Monte Carlo experiments and real data. Copyright (C) 2010 John Wiley & Sons, Ltd.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 30 (2011)
Issue (Month): 1 (January)
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
nonlinear time series ; recursive estimation ; arranged autoregression ; TAR models ; nonlinearity tests ;
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