A comparison of some estimators of time series autocorrelations

Abstract A new α-trimmed estimator is proposed for the autocorrelation function in time series analysis. This estimator is designed to increase the resistance to extreme values in the observations. The performances of the new estimator and some existing estimators are compared in a simulation study. The results indicate that the new estimator is preferable to the other alternatives when the observations are contaminated by outliers. Comments on each individual estimator are also given.

[1]  T. W. Anderson,et al.  Statistical analysis of time series , 1972 .

[2]  R. Maronna.  Robust $M$-Estimators of Multivariate Location and Scatter , 1976 .

[3]  M. Otto,et al.  Outliers in Time Series , 1972 .

[4]  Rupert G. Miller.  The jackknife-a review , 1974 .

[5]  Wai-Sum Chan,et al.  A note on time series model specification in the presence of outliers , 1992 .

[6]  B. Abraham,et al.  Outlier detection and time series modeling , 1989 .

[7]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[8]  John Makhoul.  LATTICE METHODS IN SPECTRAL ESTIMATION , 1981 .

[9]  Bovas Abraham,et al.  A SCORE TEST FOR DETECTION OF TIME SERIES OUTLIERS , 1988 .

[10]  Guido Masarotto,et al.  Robust Identification of Autoregressive Moving Average Models , 1987 .

[11]  William T. M. Dunsmuir,et al.  Asymptotic theory for time series containing missing and amplitude modulated observations , 1981 .

[12]  R. Tsay.  Time Series Model Specification in the Presence of Outliers , 1986 .

[13]  M. H. Quenouille.  Approximate Tests of Correlation in Time‐Series , 1949 .

[14]  D. G. Watts,et al.  Spectral analysis and its applications , 1968 .

[15]  B. Efron,et al.  The Jackknife: The Bootstrap and Other Resampling Plans. , 1983 .

[16]  R. Tsay.  Outliers, Level Shifts, and Variance Changes in Time Series , 1988 .

[17]  G. C. Tiao,et al.  Estimation of time series parameters in the presence of outliers , 1988 .

[18]  Steven C. Hillmer.  Monitoring and adjusting forecasts in the presence of additive outliers , 1984 .