Sparsity-Induced Global Matrix Autoregressive Model with Auxiliary Network Data
This work addresses the problem of global economic forecasting for researchers and policymakers by integrating trade network data into a matrix autoregressive framework, representing an incremental improvement over existing methods.
The authors tackled the challenge of jointly modeling and forecasting economic and financial variables across multiple countries by extending the matrix autoregression (MAR) model to incorporate international dependencies and trade network impacts, while introducing sparsity to differentiate systematic and idiosyncratic cross-predictability, with results including theoretical and empirical analyses and economic insights.
Jointly modeling and forecasting economic and financial variables across a large set of countries has long been a significant challenge. Two primary approaches have been utilized to address this issue: the vector autoregressive model with exogenous variables (VARX) and the matrix autoregression (MAR). The VARX model captures domestic dependencies, but treats variables exogenous to represent global factors driven by international trade. In contrast, the MAR model simultaneously considers variables from multiple countries but ignores the trade network. In this paper, we propose an extension of the MAR model that achieves these two aims at once, i.e., studying both international dependencies and the impact of the trade network on the global economy. Additionally, we introduce a sparse component to the model to differentiate between systematic and idiosyncratic cross-predictability. To estimate the model parameters, we propose both a likelihood estimation method and a bias-corrected alternating minimization version. We provide theoretical and empirical analyses of the model's properties, alongside presenting intriguing economic insights derived from our findings.