Data driven time scale in Gaussian quasi-likelihood inference

Shoichi Eguchi, Hiroki Masuda

研究成果: ジャーナルへの寄稿記事

抜粋

We study parametric estimation of ergodic diffusions observed at high frequency. Different from the previous studies, we suppose that sampling stepsize is unknown, thereby making the conventional Gaussian quasi-likelihood not directly applicable. In this situation, we construct estimators of both model parameters and sampling stepsize in a fully explicit way, and prove that they are jointly asymptotically normally distributed. High order uniform integrability of the obtained estimator is also derived. Further, we propose the Schwarz (BIC) type statistics for model selection and show its model-selection consistency. We conducted some numerical experiments and found that the observed finite-sample performance well supports our theoretical findings.

元の言語英語
ページ(範囲)383-430
ページ数48
ジャーナルStatistical Inference for Stochastic Processes
22
発行部数3
DOI
出版物ステータス出版済み - 10 15 2019

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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