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Hybrid estimation for ergodic diffusion processes based on noisy discrete observations

Author

Listed:
  • Yusuke Kaino

    (Osaka University)

  • Shogo H. Nakakita

    (Osaka University)

  • Masayuki Uchida

    (Osaka University
    Osaka University)

Abstract

We consider parametric estimation for ergodic diffusion processes with noisy sampled data based on the hybrid method, that is, the multi-step estimation with the initial Bayes type estimators in order to select proper initial values for optimisation of the quasi likelihood function. The asymptotic properties of the initial Bayes type estimators and the hybrid multi-step estimators are shown, and a concrete example and the simulation results are given.

Suggested Citation

  • Yusuke Kaino & Shogo H. Nakakita & Masayuki Uchida, 2020. "Hybrid estimation for ergodic diffusion processes based on noisy discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 171-198, April.
    • Handle: RePEc:spr:sistpr:v:23:y:2020:i:1:d:10.1007_s11203-019-09203-2
    DOI: 10.1007/s11203-019-09203-2
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    References listed on IDEAS

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    1. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2009. "Microstructure noise in the continuous case: The pre-averaging approach," Stochastic Processes and their Applications, Elsevier, vol. 119(7), pages 2249-2276, July.
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    5. Yoshida, Nakahiro, 1992. "Estimation for diffusion processes from discrete observation," Journal of Multivariate Analysis, Elsevier, vol. 41(2), pages 220-242, May.
    6. A. Gregorio & S. M. Iacus, 2019. "Empirical $$L^2$$ L 2 -distance test statistics for ergodic diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 233-261, July.
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    Full references (including those not matched with items on IDEAS)

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