Eigenfunction martingale estimating functions and filtered data for drift estimation of discretely observed multiscale diffusions
Abstract
We propose a novel method for drift estimation of multiscale diffusion processes when a sequence of discrete observations is given. For the Langevin dynamics in a two-scale potential, our approach relies on the eigenvalues and the eigenfunctions of the homogenized dynamics. Our first estimator is derived from a martingale estimating function of the generator of the homogenized diffusion process. However, the unbiasedness of the estimator depends on the rate with which the observations are sampled. We therefore introduce a second estimator which relies also on filtering the data and we prove that it is asymptotically unbiased independently of the sampling rate. A series of numerical experiments illustrate the reliability and efficiency of our different estimators.
Cite
@article{arxiv.2104.10587,
title = {Eigenfunction martingale estimating functions and filtered data for drift estimation of discretely observed multiscale diffusions},
author = {Assyr Abdulle and Grigorios A. Pavliotis and Andrea Zanoni},
journal= {arXiv preprint arXiv:2104.10587},
year = {2022}
}