Bayesian Infererence

dynamite: An R Package for Dynamic Multivariate Panel Models

Abstract dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising of multiple measurements per multiple individuals measured in time. The package supports joint modeling of multiple response variables, time-varying and time-invariant effects, a wide range of discrete and continuous distributions, group-specific random effects, latent factors, and customization of prior distributions of the model parameters. Models in the package are defined via a user-friendly formula interface, and estimation of the posterior distribution of the model parameters takes advantage of state-of-the-art Markov chain Monte Carlo methods.

Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models

Abstract Panel data are ubiquitous in scientific domains such as sociology and econometrics. Various modeling approaches have been presented for the analysis of such data including dynamic panel models, cross-lagged panel models, and their extensions. Existing panel data modeling approaches typically impose some restrictive assumptions on the data-generating process, such as Gaussian errors, effects that are constant in time, or univariate responses. With the goal of surmounting these restrictions, we present the dynamic multivariate panel model (DMPM) that supports both time-varying and time-invariant effects, multiple simultaneous responses across a wide variety of distributions, and arbitrary dependency structures of lagged responses of any order.