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Product autoregressive models for non-negative variables

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  • Abraham, B.
  • Balakrishna, N.

Abstract

When variables in time series context are non-negative, such as for volatility, survival time or wave heights, a multiplicative autoregressive model of the type Xt=Xt−1αVt, 0≤α<1,t=1,2,… may give the preferred dependent structure. In this paper, we study the properties of such models and propose methods for parameter estimation. Explicit solutions of the model are obtained in the case of gamma marginal distribution.

Suggested Citation

  • Abraham, B. & Balakrishna, N., 2012. "Product autoregressive models for non-negative variables," Statistics & Probability Letters, Elsevier, vol. 82(8), pages 1530-1537.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:8:p:1530-1537
    DOI: 10.1016/j.spl.2012.04.022
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    References listed on IDEAS

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    1. Maria Pacurar, 2008. "Autoregressive Conditional Duration Models In Finance: A Survey Of The Theoretical And Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 711-751, September.
    2. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," LIDAM Discussion Papers CORE 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Bauwens, Luc & Veredas, David, 2004. "The stochastic conditional duration model: a latent variable model for the analysis of financial durations," Journal of Econometrics, Elsevier, vol. 119(2), pages 381-412, April.
    4. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
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    Cited by:

    1. Balakrishna, N. & Shiji, K., 2014. "On a class of bivariate exponential distributions," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 153-160.

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