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On robust estimation of threshold autoregressions

✍ Scribed by Wai-Sum Chan; Siu-Hung Cheung

Publisher
John Wiley and Sons
Year
1994
Tongue
English
Weight
690 KB
Volume
13
Category
Article
ISSN
0277-6693

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✦ Synopsis

We investigate the effects of additive outliers on the least squares (LS) estimation of threshold autoregressive models. The class of generalized-M (GM) estimates for linear time series is modified and applied to non-linear threshold processes. A Monte Car10 experiment is carried out to study the robust properties of these estimates. Their relative forecasting performances are also examined. The results indicate that the GM method is preferable to the LS estimation when the observations are contaminated by additive outliers. A real example is also given to illustrate the proposed method.

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