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Analysing steady-state simulation output using vector autoregressive processes with exogenous variables

Author

Listed:
  • J Martens

    (Katholieke Universiteit Leuven)

  • R Peeters

    (Katholieke Universiteit Leuven)

  • F Put

    (Katholieke Universiteit Leuven)

Abstract

A simulation study often requires computation of a point estimate and confidence region for the steady-state mean of a stochastic output process. The literature offers a variety of statistical techniques, including replication/deletion, the batch-means method, and spectrum analysis. We present a new multivariate output-analysis technique that is based on the general autoregressive time-series model with exogenous variables to set up a joint confidence region for the steady-state mean. We demonstrate our technique by an extensive computational experiment, and show that it performs at least as well as other output-analysis techniques, without having some of their drawbacks.

Suggested Citation

  • J Martens & R Peeters & F Put, 2009. "Analysing steady-state simulation output using vector autoregressive processes with exogenous variables," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 696-705, May.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:5:d:10.1057_palgrave.jors.2602595
    DOI: 10.1057/palgrave.jors.2602595
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    References listed on IDEAS

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    1. John M. Charnes & W. David Kelton, 1993. "Multivariate Autoregressive Techniques for Constructing Confidence Regions on the Mean Vector," Management Science, INFORMS, vol. 39(9), pages 1112-1129, September.
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    4. George S. Fishman & Philip J. Kiviat, 1967. "The Analysis of Simulation-Generated Time Series," Management Science, INFORMS, vol. 13(7), pages 525-557, March.
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    7. Emily K. Lada & James R. Wilson & Natalie M. Steiger & Jeffrey A. Joines, 2007. "Performance of a Wavelet-Based Spectral Procedure for Steady-State Simulation Analysis," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 150-160, May.
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