Towards a general theory for non-linear locally stationary processes
Statistics Theory
2017-11-21 v3 Statistics Theory
Abstract
In this paper some general theory is presented for locally stationary processes based on the stationary approximation and the stationary derivative. Laws of large numbers, central limit theorems as well as deterministic and stochastic bias expansions are proved for processes obeying an expansion in terms of the stationary approximation and derivative. In addition it is shown that this applies to some general nonlinear non-stationary Markov-models. In addition the results are applied to derive the asymptotic properties of maximum likelihood estimates of parameter curves in such models.
Cite
@article{arxiv.1704.02860,
title = {Towards a general theory for non-linear locally stationary processes},
author = {Rainer Dahlhaus and Stefan Richter and Wei Biao Wu},
journal= {arXiv preprint arXiv:1704.02860},
year = {2017}
}
Comments
50 pages, 1 figure