Data driven time scale in Gaussian quasi-likelihood inference

Shoichi Eguchi, Hiroki Masuda

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)383-430
    Number of pages48
    JournalStatistical Inference for Stochastic Processes
    Volume22
    Issue number3
    DOIs
    Publication statusPublished - Oct 15 2019

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability

    Fingerprint

    Dive into the research topics of 'Data driven time scale in Gaussian quasi-likelihood inference'. Together they form a unique fingerprint.

    Cite this