Parameter estimation for ergodic linear SDEs from partial and discrete observations

Masahiro Kurisaki ()
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Masahiro Kurisaki: The University of Tokyo

Statistical Inference for Stochastic Processes, 2023, vol. 26, issue 2, No 3, 279-330

Abstract: Abstract We consider a problem of parameter estimation for the state space model described by linear stochastic differential equations. We assume that an unobservable Ornstein–Uhlenbeck process drives another observable process by the linear stochastic differential equation, and these two processes depend on some unknown parameters. We construct the quasi-maximum likelihood estimator of the unknown parameters and show asymptotic properties of the estimator.

Keywords: Partially observed linear model; State space model; Hidden Ornstein Uhlenbeck model; Kalman–Bucy filter; Quasi-likelihood analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11203-023-09288-w

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