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On The Selection Of Subset Autoregressive Time Series Models

Author

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  • V. Haggan
  • O. B. Oyetunji

Abstract

. 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.

Suggested Citation

  • V. Haggan & O. B. Oyetunji, 1984. "On The Selection Of Subset Autoregressive Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(2), pages 103-113, March.
    • Handle: RePEc:bla:jtsera:v:5:y:1984:i:2:p:103-113
    DOI: 10.1111/j.1467-9892.1984.tb00380.x
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    Cited by:

    1. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2004. "Estimating threshold subset autoregressive moving-average models by genetic algorithms," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 39-61.
    2. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.
    3. Chi Hua & Erxi Zhu & Liang Kuang & Dechang Pi, 2019. "Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.

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