An efficiency result for the empirical characteristic function in stationary time-series models

1990 ◽  
Vol 18 (2) ◽  
pp. 155-161 ◽  
Author(s):  
Andrey Feuerverger
Keyword(s):  
Time Series ◽  
Characteristic Function ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Empirical Characteristic Function ◽  
Stationary Time

scholarly journals Exponential tilted likelihood for stationary time series models

2021 ◽  
pp. 1-10
Author(s):  
Xiuzhen Zhang ◽  
Yukun Liu ◽  
Riquan Zhang ◽  
Zhiping Lu
Keyword(s):  
Time Series ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Stationary Time

scholarly journals SACF of the Errors of Stationary Time Series Models in the Presence of a Large Additive Outlier

2021 ◽  
pp. 31-40
Author(s):  
R. Suresh
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Stationary Time Series ◽  
Time Series Models ◽  
White Noise Process ◽  
First Order ◽  
Stationary Time ◽  
Additive Outlier ◽  
Distributed Process ◽  
Limiting Behaviour

In this paper, the limiting behaviour of the Sample Autocorrelation Function(SACF) of the errors {et} of First-Order Autoregressive (AR(1)), First-Order Moving Average (MA(1)) and First Order Autoregressive First-Order Moving Average (ARMA(1,1)) stationary time series models in the presence of a large Additive Outlier(AO) is discussed. It is found that the errors which are supposed to be uncorrelated due to either white noise process or normally distributed process are not so in the presence of a large additive outlier. The SACF of the errors follows a particular pattern based on the time series model. In the case of AR(1) model, at lag 1, the contaminated errors {et} are correlated, whereas at higher lags, they are uncorrelated. But in the MA(1) and ARMA(1,1) models, the contaminated errors {et} are correlated at all the lags. Furthermore it is observed that the intensity of correlations depends on the parameters of the respective models.

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