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Numerical Evaluation Of Distributions In Non‐Linear Autoregression

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  • R. Moeanaddin
  • Howell Tong

Abstract

. We use the Chapman‐Kolmogorov formula as a recursive relation for computing the m‐step‐ahead conditional density of a non‐linear autoregressive model. We approximate the stationary marginal probability density function of the model by the m‐step‐ahead conditional density for sufficiently large m. An advantage of our method is its simple implementation; only one NAG subroutine is needed. We have also studied the advantage of incorporating the matrix‐squaring procedure.

Suggested Citation

  • R. Moeanaddin & Howell Tong, 1990. "Numerical Evaluation Of Distributions In Non‐Linear Autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(1), pages 33-48, January.
    • Handle: RePEc:bla:jtsera:v:11:y:1990:i:1:p:33-48
    DOI: 10.1111/j.1467-9892.1990.tb00040.x
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    Cited by:

    1. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    2. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911.
    3. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    4. Garcia-Ferrer, Antonio & Queralt, Ricardo & Blazquez, Cristina, 2001. "A growth cycle characterisation and forecasting of the Spanish economy: 1970-1998," International Journal of Forecasting, Elsevier, vol. 17(3), pages 517-532.
    5. Cai, Zongwu & Fan, Jianqing, 2000. "Average Regression Surface for Dependent Data," Journal of Multivariate Analysis, Elsevier, vol. 75(1), pages 112-142, October.

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