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Optimal inference for discretely observed semiparametric Ornstein-Uhlenbeck processes

Author

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  • Marc Hallin
  • Christophe Koell
  • Bas Werker

Abstract

In this paper we discuss statistical inference about the continuous time parameters of a semiparametric Ornstein-Uhlenbeck process observed in discrete time. The model is semiparametric in the sense that we do not necessarily assume that the driving process is a Brownian motion. The main results are stated for a more general time-series model: a quantile autoregressive model. For this semiparametric model we will construct locally asymptotically efficient estimators. Finally, we investigate the implications for the semiparametric Ornstein-Uhlenbeck model.

Suggested Citation

  • Marc Hallin & Christophe Koell & Bas Werker, 2000. "Optimal inference for discretely observed semiparametric Ornstein-Uhlenbeck processes," ULB Institutional Repository 2013/2097, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:ulb:ulbeco:2013/2097
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    Cited by:

    1. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.
    2. Kristensen, Dennis, 2004. "Estimation in two classes of semiparametric diffusion models," LSE Research Online Documents on Economics 24739, London School of Economics and Political Science, LSE Library.
    3. Sonja Rieder, 2012. "Robust parameter estimation for the Ornstein–Uhlenbeck process," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(4), pages 411-436, November.
    4. Marc Hallin & Davide La Vecchia, 2014. "Semiparametrically Efficient R-Estimation for Dynamic Location-Scale Models," Working Papers ECARES ECARES 2014-45, ULB -- Universite Libre de Bruxelles.

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