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On robust estimation of threshold autoregressions
Author(s) -
Chan WaiSum,
Cheung SiuHung
Publication year - 1994
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.3980130106
Subject(s) - outlier , autoregressive model , monte carlo method , estimation , econometrics , threshold model , mathematics , statistics , series (stratigraphy) , least squares function approximation , setar , linear model , computer science , time series , star model , autoregressive integrated moving average , economics , estimator , paleontology , management , biology
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 Carlo 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.