论文信息 - ON THE SELECTION OF SUBSET AUTOREGRESSIVE TIME SERIES MODELS

ON THE SELECTION OF SUBSET AUTOREGRESSIVE TIME SERIES MODELS

. The estimation of subset autoregressive time series models has been a difficult problem because of the large number of possible alternative models involved. However, with the advent of model selection criteria based on the maximum likelihood, subset model fitting has become feasible. Using an efficient technique for evaluating the residual variance of all possible subset models, a method is proposed for the fitting of subset autoregressive models. The application of the method is illustrated by means of real and simulated data.

V. Haggan | O. B. Oyetunji

[1] A. M. Walker,et al. A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the , 1977 .

[2] H. Akaike. Statistical predictor identification , 1970 .

[3] T. Ozaki. On the Order Determination of Arima Models , 1977 .

[4] H. Akaike. A new look at the statistical model identification , 1974 .

[5] G. M. Furnival. All Possible Regressions with Less Computation , 1971 .

[6] E. Parzen. Some recent advances in time series modeling , 1974 .

[7] R. R. Hocking,et al. Selection of the Best Subset in Regression Analysis , 1967 .

[8] D. M. Allen. Mean Square Error of Prediction as a Criterion for Selecting Variables , 1971 .

[9] Howell Tong,et al. Some Comments on the Canadian Lynx Data , 1977 .

[10] M. H. Quenouille. A Large‐Sample Test for the Goodness of Fit of Autoregressive Schemes , 1947 .