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Notes on drift estimation for certain non-recurrent diffusion processes from sampled data


  • Shimizu, Yasutaka


Given discrete samples from Ornstein-Uhlenbeck processes, we consider two kinds of approximated MLE's for the drift parameter, which are asymptotically efficient in ergodic case. Our interest is the rate of convergence of those estimators when the process is non-recurrent. We add a remark when the underlying process has a slightly more general drift.

Suggested Citation

  • Shimizu, Yasutaka, 2009. "Notes on drift estimation for certain non-recurrent diffusion processes from sampled data," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2200-2207, October.
  • Handle: RePEc:eee:stapro:v:79:y:2009:i:20:p:2200-2207

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    References listed on IDEAS

    1. Kasonga, R. A., 1988. "The consistency of a non-linear least squares estimator from diffusion processes," Stochastic Processes and their Applications, Elsevier, vol. 30(2), pages 263-275, December.
    2. Dietz Hans M. & Kutoyants Yury A., 2003. "Parameter estimation for some non-recurrent solutions of SDE," Statistics & Risk Modeling, De Gruyter, vol. 21(1/2003), pages 29-46, January.
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    Cited by:

    1. Wang, Xiaohu & Yu, Jun, 2016. "Double asymptotics for explosive continuous time models," Journal of Econometrics, Elsevier, vol. 193(1), pages 35-53.
    2. Hui Jiang & Xing Dong, 2015. "Parameter estimation for the non-stationary Ornstein–Uhlenbeck process with linear drift," Statistical Papers, Springer, vol. 56(1), pages 257-268, February.
    3. Bercu, Bernard & Coutin, Laure & Savy, Nicolas, 2012. "Sharp large deviations for the non-stationary Ornstein–Uhlenbeck process," Stochastic Processes and their Applications, Elsevier, vol. 122(10), pages 3393-3424.

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