Abstract
Threshold autoregressive (TAR) models have been widely used for periodic time series, as they are nonlinear models relatively simple to handle being linear in different regions of the state space, see e.g. Tong (1990). One of the main problems regards the decision of the selection of the correct order of a TAR model. This problem has been addressed by Tong (1983), Wong and Li (1998) and De Gooijer (2001). Wong and Li proposed the Akaike information criterion (AIC), the biased corrected version AICc the AICu which is an approximately unbiased estimate of the Kullback-Leibler information and the Bayesian information criterion (BIC). De Gooijer proposed a cross validation criterion (CUu *) corresponding to AICu.
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© 2002 Springer-Verlag Berlin Heidelberg
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Öhrvik, J., Schoier, G. (2002). Bootstrapping Threshold Autoregressive Models. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_27
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DOI: https://doi.org/10.1007/978-3-642-57489-4_27
Publisher Name: Physica, Heidelberg
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