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Antithetic time series analysis and the CompanyX data

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  • Dennis Ridley
  • Pierre Ngnepieba

Abstract

type="main" xml:lang="en"> Summary. Antithetic time series analysis is the solution to a most perplexing problem in mathematical statistics. When a mathematical model is fitted to serially correlated data, the parameters of the model are unavoidably biased. All forecasts that are obtained from the model are unavoidably biased and therefore diverge. The forecast reliability worsens with the forecast horizon. It is shown that the forecast bias can be dynamically reduced. This is made possible by the entirely counterintuitive discovery of antithetic time series theory that permits unbiased forecast error convergence to a constant, independent of forecast origin. The forecast error variance in each time period is the same.

Suggested Citation

  • Dennis Ridley & Pierre Ngnepieba, 2014. "Antithetic time series analysis and the CompanyX data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 83-94, January.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:1:p:83-94
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    File URL: http://hdl.handle.net/10.1111/j.1467-985X.2012.12001.x
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    References listed on IDEAS

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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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