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Inference for random coefficient volatility models


  • Thavaneswaran, A.
  • Liang, You
  • Frank, Julieta


Estimating functions have been shown to be convenient to study inference for nonlinear time series models. One such model is the recently proposed Random Coefficient Autoregressive (RCA) model with Generalized Autoregressive Heteroscedasticity (GARCH) errors (Thavaneswaran et al., 2009). We derive the martingale estimating functions for the joint estimation of the conditional mean and variance parameters and we show the information gain relative to conditional least square estimation.

Suggested Citation

  • Thavaneswaran, A. & Liang, You & Frank, Julieta, 2012. "Inference for random coefficient volatility models," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2086-2090.
  • Handle: RePEc:eee:stapro:v:82:y:2012:i:12:p:2086-2090 DOI: 10.1016/j.spl.2012.07.008

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    References listed on IDEAS

    1. Girard, Stéphane & Jacob, Pierre, 2008. "Frontier estimation via kernel regression on high power-transformed data," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 403-420, March.
    2. Peter Hall & Julian Z. Wang, 2005. "Bayesian likelihood methods for estimating the end point of a distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 717-729.
    3. Deyuan Li & Liang Peng & Yongcheng Qi, 2011. "Empirical likelihood confidence intervals for the endpoint of a distribution function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 353-366, August.
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    Cited by:

    1. Aerambamoorthy Thavaneswaran & Nalini Ravishanker & You Liang, 2015. "Generalized duration models and optimal estimation using estimating functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(1), pages 129-156, February.


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