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Parameter estimation for the Rosenblatt Ornstein–Uhlenbeck process with periodic mean

Radomyra Shevchenko () and Ciprian A. Tudor ()
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Radomyra Shevchenko: TU Dortmund
Ciprian A. Tudor: CNRS, Université de Lille

Statistical Inference for Stochastic Processes, 2020, vol. 23, issue 1, No 8, 227-247

Abstract: Abstract We study the least squares estimator for the drift parameter of the Langevin stochastic equation driven by the Rosenblatt process. Using the techniques of the Malliavin calculus and the stochastic integration with respect to the Rosenblatt process, we analyze the consistency and the asymptotic distribution of this estimator. We also introduce alternative estimators, which can be simulated, and we study their asymptotic properties.

Keywords: Rosenblatt process; Parameter estimation; Malliavin calculus; Multiple Wiener–Itô integrals; Strong consistency; Asymptotic normality; Ornstein–Uhlenbeck process; Periodic mean function; Least squares estimator; 60H15; 60H07; 60G35 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11203-019-09200-5

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