Outlier Detection And Estimation In NonLinear Time Series
- 6 January 2005
- journal article
- Published by Wiley in Journal of Time Series Analysis
- Vol. 26 (1) , 107-121
Abstract
Abstract. The problem of identifying the time location and estimating the amplitude of outliers in nonlinear time series is addressed. A model‐based method is proposed for detecting the presence of additive or innovational outliers when the series is generated by a general nonlinear model. We use this method for identifying and estimating outliers in bilinear, self‐exciting threshold autoregressive and exponential autoregressive models. A simulation study is performed to test the proposed procedures and comparing them with the methods based on linear models and linear interpolators. Finally, our results are applied for detecting outliers in the Canadian lynx trappings and in the sunspot numbers data.Keywords
This publication has 26 references indexed in Scilit:
- Testing for ARCH in the presence of additive outliersJournal of Applied Econometrics, 1999
- Robust estimation of bilinear time series modelsCommunications in Statistics - Theory and Methods, 1998
- On robust estimation of threshold autoregressionsJournal of Forecasting, 1994
- Joint Estimation of Model Parameters and Outlier Effects in Time SeriesJournal of the American Statistical Association, 1993
- Forecasting time series with outliersJournal of Forecasting, 1993
- Least squates estimation of missing values in time seriesCommunications in Statistics - Theory and Methods, 1992
- Interpolation, outliers mid inverse autocorrelationsCommunications in Statistics - Theory and Methods, 1991
- A Note on the Estimation of Missing Values in Time SeriesCommunications in Statistics - Simulation and Computation, 1989
- Estimation of Time Series Parameters in the Presence of OutliersTechnometrics, 1988
- Intervention Analysis with Applications to Economic and Environmental ProblemsJournal of the American Statistical Association, 1975