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Preliminary test estimation for spectra

Author

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  • Maeyama, Yusuke
  • Tamaki, Kenichiro
  • Taniguchi, Masanobu

Abstract

For a general non-Gaussian stationary linear process, quasi-maximum likelihood estimation of a subset of the parameters of the spectral density is considered when the complementary subset is suspected to be superfluous. A preliminary test quasi-maximum likelihood estimator (q-MLE) of parameters is introduced and, in the light of its mean square error, is compared with the restricted and unrestricted q-MLE.

Suggested Citation

  • Maeyama, Yusuke & Tamaki, Kenichiro & Taniguchi, Masanobu, 2011. "Preliminary test estimation for spectra," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1580-1587, November.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:11:p:1580-1587
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    References listed on IDEAS

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    1. Taniguchi, Masanobu, 1985. "An Asymptotic Expansion for the Distribution of the Likelihood Radio Criterion for a Gaussian Autoregressive Moving Average Process Under a Local Alternative," Econometric Theory, Cambridge University Press, vol. 1(1), pages 73-84, April.
    2. Marc Hallin & Masanobu Taniguchi & Abdeslam Serroukh & Kokyo Choy, 1999. "Local asymptotic normality for regression models with long-memory disturbance, with statistical applications," ULB Institutional Repository 2013/2091, ULB -- Universite Libre de Bruxelles.
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    Cited by:

    1. Ejaz Ahmed, S. & Fallahpour, Saber, 2012. "Shrinkage estimation strategy in quasi-likelihood models," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2170-2179.
    2. Paindaveine, Davy & Rasoafaraniaina, Rondrotiana Joséa & Verdebout, Thomas, 2017. "Preliminary test estimation for multi-sample principal components," Econometrics and Statistics, Elsevier, vol. 2(C), pages 106-116.
    3. Davy Paindaveine & Joséa Rasoafaraniaina & Thomas Verdebout, 2021. "Preliminary test estimation in uniformly locally asymptotically normal models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 689-707, June.

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