Multivariate spatio-temporal models for high-dimensional areal data with application to Longitudinal Employer-Household Dynamics

TL;DR

This paper introduces a Bayesian multivariate spatio-temporal mixed effects model (MSTM) designed to efficiently analyze high-dimensional areal data, such as US Census Bureau's Quarterly Workforce Indicators, enabling estimation of missing data with uncertainty quantification.

Contribution

The paper develops the MSTM, extending Moran's I basis functions to multivariate spatio-temporal data, providing effective dimension reduction and a dynamic modeling framework for high-dimensional areal datasets.

Findings

Successfully models high-dimensional QWI data

Enables estimation of missing values with uncertainty

Reduces computational complexity for large datasets

Abstract

Many data sources report related variables of interest that are also referenced over geographic regions and time; however, there are relatively few general statistical methods that one can readily use that incorporate these multivariate spatio-temporal dependencies. Additionally, many multivariate spatio-temporal areal data sets are extremely high dimensional, which leads to practical issues when formulating statistical models. For example, we analyze Quarterly Workforce Indicators (QWI) published by the US Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) program. QWIs are available by different variables, regions, and time points, resulting in millions of tabulations. Despite their already expansive coverage, by adopting a fully Bayesian framework, the scope of the QWIs can be extended to provide estimates of missing values along with associated measures of uncertainty. Motivated by the LEHD, and other applications in federal statistics, we introduce the multivariate spatio-temporal mixed effects model (MSTM), which can be used to efficiently model high-dimensional multivariate spatio-temporal areal data sets. The proposed MSTM extends the notion of Moran's I basis functions to the multivariate spatio-temporal setting. This extension leads to several methodological contributions, including extremely effective dimension reduction, a dynamic linear model for multivariate spatio-temporal areal processes, and the reduction of a high-dimensional parameter space using a novel parameter model.