A Robust Approach to ARMA Factor Modeling
Abstract
This paper deals with the dynamic factor analysis problem for an ARMA process. To robustly estimate the number of factors, we construct a confidence region centered in a finite sample estimate of the underlying model which contains the true model with a prescribed probability. In this confidence region, the problem, formulated as a rank minimization of a suitable spectral density, is efficiently approximated via a trace norm convex relaxation. The latter is addressed by resorting to the Lagrange duality theory, which allows to prove the existence of solutions. Finally, a numerical algorithm to solve the dual problem is presented. The effectiveness of the proposed estimator is assessed through simulation studies both with synthetic and real data.
Cite
@article{arxiv.2107.03873,
title = {A Robust Approach to ARMA Factor Modeling},
author = {Lucia Falconi and Augusto Ferrante and Mattia Zorzi},
journal= {arXiv preprint arXiv:2107.03873},
year = {2021}
}