Homogeneity Fusion for Sparse Grouped Network VAR Models

Music City Center 

We consider the problem of simultaneous parameter estimation, group detection, and network recovery in the Grouped Network VAR model under the setting where the underlying network is sparse and unknown. Building on recent advances in homogeneity fusion for structured regression, we propose a fusion estimator that simultaneously recovers the true group and network structures with high probability, and derive nonasymptotic concentration bounds for the corresponding estimation error. On the implementation side, we formulate our estimation optimization problem as a mixed integer program and propose an iterative algorithm to solve it at scale. We evaluate the finite-sample performance of our estimator on synthetic data and validate its practical significance on two real datasets from macroeconomics and finance.