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Projection Modulus: A New Direction For Selecting Subset Autoregressive Models

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  • Xichuan Zhang
  • R. Deane Terrell

Abstract

Selecting a parsimonious subset autoregressive time series model is a valuable objective particularly where there is or may be evidence that a time series may have some form of periodic or quasi‐periodic behaviour. An efficient model selection procedure is essential because of the large number of possible alternative models involved. The explanation of an increase in residual variance due to excluding a lag is examined in Hilbert space. As a result, a new statistic, the projection modulus, and its derivatives are developed to assess the significance of any lag in a model. The impact of deleting a lag, as measured by these statistics, helps to produce a selection procedure where true lags have less chance of being removed. We then assess an efficient subset autoregressive model selection procedure employing these statistics. The success of the proposed procedure is illustrated by its efficiency in identifying the true model for simulated and real data.

Suggested Citation

  • Xichuan Zhang & R. Deane Terrell, 1997. "Projection Modulus: A New Direction For Selecting Subset Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(2), pages 195-212, March.
    • Handle: RePEc:bla:jtsera:v:18:y:1997:i:2:p:195-212
    DOI: 10.1111/1467-9892.00046
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    Cited by:

    1. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.

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