Parameter estimation of Ornstein–Uhlenbeck process generating a stochastic graph

Emmanuel Gobet () and Gustaw Matulewicz ()
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Emmanuel Gobet: Université Paris Saclay
Gustaw Matulewicz: Université Paris Saclay

Statistical Inference for Stochastic Processes, 2017, vol. 20, issue 2, No 3, 235 pages

Abstract: Abstract Given Y a graph process defined by an incomplete information observation of a multivariate Ornstein–Uhlenbeck process X, we investigate whether we can estimate the parameters of X. We define two statistics of Y. We prove convergence properties and show how these can be used for parameter inference. Finally, numerical tests illustrate our results and indicate possible extensions and applications.

Keywords: Stochastic graph process; Inference for stochastic process; Incomplete information; Asymptotic properties of estimators; 62Mxx; 05C80; 62F12 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11203-016-9142-4

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