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Inference for random coefficient volatility models

Author

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

Abstract

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

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    1. A. Thavaneswaran & B. Abraham, 1988. "Estimation For Non‐Linear Time Series Models Using Estimating Equations," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(1), pages 99-108, January.
    2. Julieta Frank & Melody Ghahramani & Aera Thavaneswaran, 2011. "Recent Developments in Seasonal Volatility Models," Chapters, in: Miroslav Verbic (ed.), Advances in Econometrics - Theory and Applications, IntechOpen.
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    Citations

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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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