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A simulation study of structure characterization methods

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  • Spriet, J.A.
  • Herman, P.

Abstract

Linear black box modelling often requires an inductive model structure characterization method or expressed in other terms a technique that permits for the determination of the order of the model on the basis of experimental data. Recently a number of different methods like FPE, AIC, LILC and BIC have been proposed. The statistical properties of those criteria are badly known. In the paper, the criteria are shown to be similar, differing only in a threshold level. A systematic Monte-Carlo simulation study with second order autoregressive models has been carried out. It is found that the properties of the structure discriminating statistics are remarkably independent of the parameter values or the location of the poles. For all criteria, the error rate increases as the coefficient of the highest order time lag decreases to zero. The simulation indicates the superiority of BIC over LILC and of LILC over AIC. The study suggests that a new criterium that would be dependent on the highest order time lag parameter could be superior.

Suggested Citation

  • Spriet, J.A. & Herman, P., 1983. "A simulation study of structure characterization methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 25(5), pages 452-459.
    • Handle: RePEc:eee:matcom:v:25:y:1983:i:5:p:452-459
    DOI: 10.1016/0378-4754(83)90143-X
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    Cited by:

    1. Freeman, T.Graham, 1985. "Selecting the best model to fit data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 27(2), pages 137-140.

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