Noise inference for ergodic Lévy driven SDE

Hiroki Masuda, Lorenzo Mercuri, Yuma Uehara

    Research output: Contribution to journalArticlepeer-review


    We study inference for the driving Lévy noise of an ergodic stochastic differential equation (SDE) model, when the process is observed at high-frequency and long time and when the drift and scale coefficients contain finite-dimensional unknown parameters. By making use of the Gaussian quasi-likelihood function for the coefficients, we derive a stochastic expansion for functionals of the unit-time residuals, which clarifies some quantitative effect of plugging in the estimators of the coefficients, thereby enabling us to take several inference procedures for the driving-noise characteristics into account. We also present new classes and methods available in YUIMA for the simulation and the estimation of a Lévy SDE model. We highlight the flexibility of these new advances in YUIMA using simulated and real data.

    Original languageEnglish
    Pages (from-to)2432-2474
    Number of pages43
    JournalElectronic Journal of Statistics
    Issue number1
    Publication statusPublished - 2022

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Statistics, Probability and Uncertainty


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