Parameter estimation for threshold Ornstein-Uhlenbeck processes from discrete observations
Hu, Yaozhong 1Xi, Yuejuan2
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作者信息
1. Univ Alberta Edmonton
2. Nankai Univ
折叠
Abstract
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.
Key words
Threshold Ornstein-Uhlenbeck process/Invariant measure/Ergodic theorem/Generalized moment estimators/Strong consistency/Asymptotic normality/LIKELIHOOD-ESTIMATION/STABILITY/MODELS
NSTL
Elsevier