Density-valued VAR Models with Latent Factors

TL;DR

This paper introduces a density-valued VAR model with latent factors for analyzing multivariate time series of density functions, applied to SARS-CoV-2 Ct value distributions across Brazilian regions.

Contribution

It develops a novel modeling approach combining density estimation, transformation, and VAR with latent factors to separate common and idiosyncratic regional dynamics.

Findings

Simulations show increasing factors mainly removes spurious edges.

Weak network in full sample; stronger, directed network when excluding first six months.

Detected directed links from northern to southeastern regions.

Abstract

We propose a density-valued vector autoregressive model with latent factors for multivariate time series of density functions. Motivated by weekly regional distributions of SARS-CoV-2 cycle threshold (Ct) values in Brazil, we study their distributional dynamics across regions. The Ct value is the number of amplification cycles required for the viral signal to cross a detection threshold (lower Ct values correspond to higher viral load). We estimate each regional density by a B-spline mixture, mapping the mixture weights to a Euclidean space by a generalized logit transform equipped with an isometric inner product, and model the transformed series by a cross-regional VAR with latent factors. This decomposition allows for the separation between strong common movements and directed idiosyncratic dynamics. Directed edges are identified from the idiosyncratic VAR component using one-sided tests with Benjamini--Yekutieli false discovery rate control. Simulations show that increasing the number of estimated factors does not mechanically eliminate genuine idiosyncratic dependence; rather, it mainly removes spuriously detected edges driven by common factor movements. In the real-world data application, the full sample yields only a weak directed network, whereas a substantial network emerges once the first six months are excluded and the density prior is kept weak. The estimated links suggest directed predictive relations from the northern region toward southeastern metropolitan areas.