Learning AR factor models
Francesca Crescente, Lucia Falconi, Federica Rozzi, Augusto Ferrante, and Mattia Zorzi
TL;DR
This paper introduces an auto-regressive factor analysis method using approximate moment matching, which iteratively estimates the number of factors and AR parameters, demonstrating effectiveness through simulations.
Contribution
It presents a novel iterative algorithm combining factor analysis and AR estimation for improved factor model learning.
Findings
Effective estimation demonstrated in simulation studies
Algorithm accurately determines number of factors
Combines factor analysis with AR dynamics estimation
Abstract
We face the factor analysis problem using a particular class of auto-regressive processes. We propose an approximate moment matching approach to estimate the number of factors as well as the parameters of the model. This algorithm alternates a step of factor analysis and a step of AR dynamics estimation. Some simulation studies show the effectiveness of the proposed estimator.
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