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Advances in antithetic time series analysis: separating fact from artifact

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  • Dennis Ridley

Abstract

The problem of biased time series mathematical model parameter estimates is well known to be insurmountable. When used to predict future values by extrapolation, even a de minimis bias will eventually grow into a large bias, with misleading results. This paper elucidates how combining antithetic time series’ solves this baffling problem of bias in the fitted and forecast values by dynamic bias cancellation. Instead of growing to infinity, the average error can converge to a constant.

Suggested Citation

  • Dennis Ridley, 2016. "Advances in antithetic time series analysis: separating fact from artifact," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 26(3), pages 57-68.
    • Handle: RePEc:wut:journl:v:3:y:2016:p:57-68:id:1230
    DOI: 10.5277/ord160304
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    References listed on IDEAS

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    1. Li, Bo & Nychka, Douglas W. & Ammann, Caspar M., 2010. "The Value of Multiproxy Reconstruction of Past Climate," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 883-895.
    2. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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    Cited by:

    1. Dennis Ridley & Pierre Ngnepieba, 2023. "Antithetic Power Transformation in Monte Carlo Simulation: Correcting Hidden Errors in the Response Variable," Mathematics, MDPI, vol. 11(9), pages 1-12, April.

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