Structured Estimation of Heterogeneous Time Series

• URL: http://arxiv.org/abs/2311.08658v1
• Date: Wed, 15 Nov 2023 02:39:13 GMT
• Title: Structured Estimation of Heterogeneous Time Series
• Authors: Zachary F. Fisher and Younghoon Kim and Vladas Pipiras and Christopher Crawford and Daniel J. Petrie and Michael D. Hunter and Charles F. Geier
• Abstract summary: 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. 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. We extend the multi- VAR framework to include new adaptive weighting schemes that greatly improve estimation performance.
• Score: 5.102931012520635
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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