Model-free Bootstrap Prediction Regions for Multivariate Time Series
Abstract
In Das and Politis(2020), a model-free bootstrap(MFB) paradigm was proposed for generating prediction intervals of univariate, (locally) stationary time series. Theoretical guarantees for this algorithm was resolved in Wang and Politis(2019) under stationarity and weak dependence condition. Following this line of work, here we extend MFB for predictive inference under a multivariate time series setup. We describe two algorithms, the first one works for a particular class of time series under any fixed dimension d; the second one works for a more generalized class of time series under low-dimensional setting. We justify our procedure through theoretical validity and simulation performance.
Cite
@article{arxiv.2112.08671,
title = {Model-free Bootstrap Prediction Regions for Multivariate Time Series},
author = {Yiren Wang and Dimitris N. Politis},
journal= {arXiv preprint arXiv:2112.08671},
year = {2021}
}
Comments
This is an initial version of the paper. A generalization to our setting is under investigation