We present a new framework for learning Granger causality networks for
multivariate categorical time series, based on the mixture transition
distribution (MTD) model. Traditionally, MTD is plagued by a nonconvex
objective, non-identifiability, and presence of many local optima. To
circumvent these problems, we recast inference in the MTD as a convex problem.
The new formulation facilitates the application of MTD to high-dimensional
multivariate time series. As a baseline, we also formulate a multi-output
logistic autoregressive model (mLTD), which while a straightforward extension
of autoregressive Bernoulli generalized linear models, has not been previously
applied to the analysis of multivariate categorial time series. We develop
novel identifiability conditions of the MTD model and compare them to those for
mLTD. We further devise novel and efficient optimization algorithm for the MTD
based on the new convex formulation, and compare the MTD and mLTD in both
simulated and real data experiments. Our approach simultaneously provides a
comparison of methods for network inference in categorical time series and
opens the door to modern, regularized inference with the MTD model.