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- W59554992 abstract "This chapter discusses the concept of outlier detection in stationary and nonstationary time series models and the effects of outliers on unit root tests. It also discusses the different robust unit root tests suggested in the literature and the robust estimation of nonstationary data. Many of the time series in economics and finance are characterized by errors with heavy tailed distributions. There are two approaches to outlier detection. The first is the classical approach starting with the paper by Fox, and later extended by several others. The second is the Bayesian approach, first started by Abraham and Box. Outlier detection is important for methods based on discarding the outliers. The other alternative of leaving the outliers in and using robust methods does not always depend on outlier detection; the chapter describes the way the robust methods can be used for outlier detection." @default.
- W59554992 created "2016-06-24" @default.
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- W59554992 date "1997-01-01" @default.
- W59554992 modified "2023-10-14" @default.
- W59554992 title "10 Outliers, unit roots and robust estimation of nonstationary time series" @default.
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- W59554992 doi "https://doi.org/10.1016/s0169-7161(97)15012-x" @default.
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