首页|Parameter estimation for threshold Ornstein-Uhlenbeck processes from discrete observations
Parameter estimation for threshold Ornstein-Uhlenbeck processes from discrete observations
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
Assuming that a threshold Ornstein-Uhlenbeck process is observed at discrete time instants, we propose generalized moment estimators to estimate the parameters. Our theoretical basis is the celebrated ergodic theorem. With the sampling time step arbitrarily fixed, we prove the strong consistency and asymptotic normality of our estimators as the sample size tends to infinity. (C) 2022 Elsevier B.V. All rights reserved.
Threshold Ornstein-Uhlenbeck processInvariant measureErgodic theoremGeneralized moment estimatorsStrong consistencyAsymptotic normalityLIKELIHOOD-ESTIMATIONSTABILITYMODELS
NSTL
Elsevier
