A0625
Title: Sparsity-induced global matrix autoregressive model for forecasting global economy
Authors: Sanyou Wu - City University of Hong Kong (Hong Kong)
Dan Yang - University of Hong Kong (Hong Kong) [presenting]
Yan Xu - The University of Hong Kong (Hong Kong)
Long Feng - City University of Hong Kong (Hong Kong)
Abstract: 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. An extension of the MAR model is proposed 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, a sparse component is introduced to the model to differentiate between systematic and idiosyncratic cross-predictability. To estimate the model parameters, both a likelihood estimation method and a bias-corrected alternating minimization version are proposed. Theoretical and empirical analyses of the model's properties are provided, alongside presenting intriguing economic insights derived from findings.
