Noise Inference For Ergodic Lévy Driven SDE

by   Hiroki Masuda, et al.

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.


page 1

page 2

page 3

page 4


Bootstrap method for misspecified ergodic Lévy driven stochastic differential equation models

In this paper, we consider possibly misspecified stochastic differential...

Gaussian quasi-information criteria for ergodic Lévy driven SDE

We consider relative model comparison for the parametric coefficients of...

Parameter estimation for a linear parabolic SPDE model in two space dimensions with a small noise

We study parameter estimation for a linear parabolic second-order stocha...

Data driven time scale in Gaussian quasi-likelihood inference

We study parametric estimation of ergodic diffusions observed at high fr...

Adaptive estimator for a parabolic linear SPDE with a small noise

We deal with parametric estimation for a parabolic linear second order s...

High-frequency Estimation of the Lévy-driven Graph Ornstein-Uhlenbeck process

We consider the Graph Ornstein-Uhlenbeck (GrOU) process observed on a no...

Please sign up or login with your details

Forgot password? Click here to reset