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arXiv · 2107.03873

A Robust Approach to ARMA Factor Modeling

Lucia Falconi·Augusto Ferrante·Mattia Zorzi
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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.

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Lucia Falconi, Augusto Ferrante, Mattia Zorzi. 2021-07-08. A Robust Approach to ARMA Factor Modeling. https://arxiv.org/abs/2107.03873

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