Dynamic graphical models: Theory, structure and counterfactual forecasting

Music City Center 

Simultaneous graphical dynamic linear models (SGDLMs) provide advances in flexibility, parsimony and scalability of multivariate time series analysis, with proven utility in forecasting. Core theoretical aspects of such models are developed, including new results linking dynamic graphical and latent factor models. Methodological developments extend existing Bayesian sequential analyses for model marginal likelihood evaluation and counterfactual forecasting. The latter, involving new Bayesian computational developments for missing data in SGDLMs, is motivated by causal applications. A detailed example concerns the effect of the Affordable Care Act's Medicaid expansion on employment, with advances in model uncertainty and staggered adoptions.

This is joint work with Mike West.

Bayesian forecasting

Causal inference

Counterfactual forecasting

Dynamic graphical models

Multivariate time series

Outcome adaptive models