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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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    1. Armstrong, J. Scott, 1989. "Combining forecasts: The end of the beginning or the beginning of the end?," International Journal of Forecasting, Elsevier, vol. 5(4), pages 585-588.
    2. Calzolari, Giorgio, 1979. "Antithetic variates to estimate the simulation bias in non-linear models," Economics Letters, Elsevier, vol. 4(4), pages 323-328.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Nerlove, Marc, 1958. "Distributed Lags and Demand Analysis for Agricultural and Other Commodities," Technical Resources 316559, United States Department of Agriculture, Agricultural Marketing Service, Transportation and Marketing Program.
    5. 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.
    6. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
    7. Chatfield, Chris, 1988. "Apples, oranges and mean square error," International Journal of Forecasting, Elsevier, vol. 4(4), pages 515-518.
    8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, Decembrie.
    10. Fisher, Paul & Salmon, Mark, 1986. "On Evaluating the Importance of Nonlinearity in Large Macroeconometric Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 27(3), pages 625-646, October.
    11. Jack P. C. Kleijnen, 1975. "Antithetic Variates, Common Random Numbers and Optimal Computer Time Allocation in Simulation," Management Science, INFORMS, vol. 21(10), pages 1176-1185, June.
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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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