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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. 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.
    2. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.
    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. 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.

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