Joint Mean-Vector and Var-Matrix estimation for Locally Stationary VAR(1) processes
Methodology
2021-04-26 v1 Statistics Theory
Statistics Theory
Abstract
During the last two decades, locally stationary processes have been widely studied in the time series literature. In this paper we consider the locally-stationary vector-auto-regression model of order one, or LS-VAR(1), and estimate its parameters by weighted least squares. The LS-VAR(1) we consider allows for a smoothly time-varying non-diagonal VAR matrix, as well as for a smoothly time-varying non-zero mean. The weighting scheme is based on kernel smoothers. The time-varying mean and the time-varying VAR matrix are estimated jointly, and the definition of the local-linear weighting matrix is provided in closed-from. The quality of the estimated curves is illustrated through simulation results.
Cite
@article{arxiv.2104.11358,
title = {Joint Mean-Vector and Var-Matrix estimation for Locally Stationary VAR(1) processes},
author = {Giovanni Motta},
journal= {arXiv preprint arXiv:2104.11358},
year = {2021}
}