Optimal stable Ornstein–Uhlenbeck regression

Autor: Hiroki Masuda
Rok vydání: 2023
Předmět:
Zdroj: Japanese Journal of Statistics and Data Science. 6:573-605
ISSN: 2520-8764
2520-8756
DOI: 10.1007/s42081-023-00197-z
Popis: We prove asymptotically efficient inference results concerning an Ornstein–Uhlenbeck regression model driven by a non-Gaussian stable Lévy process, where the output process is observed at high frequency over a fixed period. The local asymptotics of non-ergodic type for the likelihood function is presented, followed by a way to construct an asymptotically efficient estimator through a suboptimal, yet very simple preliminary estimator.
Databáze: OpenAIRE
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