Structured Estimation of Heterogeneous Time Series
Abstract
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al., (2022) introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation. This approach differs from many popular modeling approaches for multiple-subject time series in that qualitative and quantitative differences in a large number of individual dynamics are well-accommodated. The current work extends the multi-VAR framework to include new adaptive weighting schemes that greatly improve estimation performance. In a small set of simulation studies we compare adaptive multi-VAR with these new penalty weights to common alternative estimators in terms of path recovery and bias. Furthermore, we provide toy examples and code demonstrating the utility of multi-VAR under different heterogeneity regimes using the multivar package for R (Fisher, 2022).
Cite
@article{arxiv.2311.08658,
title = {Structured Estimation of Heterogeneous Time Series},
author = {Zachary F. Fisher and Younghoon Kim and Vladas Pipiras and Christopher Crawford and Daniel J. Petrie and Michael D. Hunter and Charles F. Geier},
journal= {arXiv preprint arXiv:2311.08658},
year = {2023}
}