Selection of a multistep linear predictor for short time series

Statistica Sinica

Volumn 7, Issue 2, 1997, Pages 395-406

  Hurvich, Clifford M ^a   Tsai, Chih Ling ^b  

^a NEW YORK UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

AIC c; Burg's estimator; Kullback Leibler information

Indexed keywords

EID: 0031525657     PISSN: 10170405     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (22)

1. Akaike, H. (1970). Statistical predictor identification. Ann. Inst. Statist. Math. 22, 203-217.
2. Akaike, H. (1973). Information theory and an extension of the maximun likelihood principle. In 2nd International Symposium on Information Theory (Edited by B. N. Petrov and F. Csáki), 267-281. Akadémiai Kiadó, Budapest.
3. Beamish, N. and Priestley, M. B. (1981). A study of autoregressive and window spectral estimation. Appl. Statist. 30, 41-58.
4. Bhansali, R (1996). Asymptotically efficient autoregressive model selection for multistep prediction. Ann. Inst. Statist. Math. 48, 577-602.
5. Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second Edition. Springer-Verlag, New York.
6. Burg, J. P. (1978.) A new analysis technique for time series data. In Modern Spectrum Analysis (Edited by D. G. Childers), 42-48. IEEE Press, New York.
7. Chen, C., Davis, R. A., Brockwell, P. J. and Bai, Z. D. (1993). Order determination for autoregressive processes using resampling methods. Statist. Sinica 3, 481-500.
8. Findley, D. F. (1983). On using a different time series forecasting model for each forecast lead. Statistical Research Division Report No. CENSUS/SRD/RR/-83/06, Bureau of Census, Washington, D. C.
9. Hainz, G. (1995). The asymptotic properties of Burg estimators. Preprint Univ. of Heidelberg.
10. Haykin, S. (ed.) (1983). Nonlinear methods of spectral analysis. Topics in Applied Physics, Vol. 34, Second Ed., Springer-Verlag, New York.
11. Hurvich, C. M. (1987). Automatic selection of a linear predictor through frequency domain cross-validation. Comm. Statist. Theory Mehtods 16, 3199-3234.
12. Hurich, C. M. and Tsai, C.-L. (1989). Regression and time series model selection in small samples. Biometrika 76, 297-307.
13. Hurvich, C. M. and Tsai, C.-L. (1991). Bias of the corrected AIC criterion for underfitted regression and time series models. Biometrika 78, 499-509.
14. Linhart, H. and Zucchini, W. (1986). Model Selection. John Wiley, New York.
15. Priestley, M. B. (1981). Spectral Analysis And Time Series. Academic Press, New York.
16. Shaman, P. (1983). Properties of estimates of the mean square error of prediction in autoregressive models. In Studies in Econometrics, Time Series and Multivariate Statistics (Edited by S. Karlin, T. Amemiya, L. A. Goodman), 331-342. Academic Press, New York.
17. Shaman, P. and Stine, R. A. (1988). The bias of autoregressive coefficient estimators. J. Amer. Statist. Assoc. 83, 842-848.
18. Shibata, R. (1980). Asymptotically efficient selection of the order of the model for estimating parameters of a linear process. Ann Statist. 8, 147-164.
19. Tiao, G. C. and Xu, D. (1993). Robustness of MLE for multi-step predictions: the exponential smoothing case. Biometrika 80, 623-641.
20. Tiao, G. C. and Tsay, R. S. (1994). Some advances in non-linear and adaptive modelling in time series. J. Forecasting 13, 109-140.
21. Tjøstheim, D. and Paulsen, J. (1983). Bias of some commonly-used time series estimates. Biometrika 70, 389-399.
22. Wei, W. S. (1990). Time Series Analysis. Addison-Wesley, New York.