Large Spillover Networks of Nonstationary Systems

This article proposes a vector error correction framework for constructing large consistent spillover networks of nonstationary systems grounded in the network theory of Diebold and Y ilmaz. We aim to provide a tailored methodology for the large nonstationary (macro)economic and financial system application settings avoiding technical and often hard to verify assumptions for general statistical high-dimensional approaches where the dimension can also increase with sample size. To achieve this, we propose an elementwise Lasso-type technique for consistent and numerically efficient model selection of VECM, and relate the resulting forecast error variance decomposition to the network topology representation. We also derive the corresponding asymptotic results for model selection and network estimation under standard assumptions. Moreover, we develop a refinement strategy for efficient estimation and show implications and modifications for general dependent innovations. In a comprehensive simulation study, we show convincing finite sample performance of our technique in all cases of moderate and low dimensions. In an application to a system of FX rates, the proposed method leads to novel insights on the connectedness and spillover effects in the FX market among the OECD countries.