Estimators of diffusions with randomly spaced discrete observations: A general theory
Abstract
We provide a general method to analyze the asymptotic properties of a variety of estimators of continuous time diffusion processes when the data are not only discretely sampled in time but the time separating successive observations may possibly be random. We introduce a new operator, the generalized infinitesimal generator, to obtain Taylor expansions of the asymptotic moments of the estimators. As a special case, our results apply to the situation where the data are discretely sampled at a fixed nonrandom time interval. We include as specific examples estimators based on maximum-likelihood and discrete approximations such as the Euler scheme.
Cite
@article{arxiv.math/0503679,
title = {Estimators of diffusions with randomly spaced discrete observations: A general theory},
author = {Yacine Ait-Sahalia and Per A. Mykland},
journal= {arXiv preprint arXiv:math/0503679},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/009053604000000427 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)