Abstract
AbstractTo enhance the measurement of economic and financial spillovers, we bring together the spatial and global vector autoregressive (GVAR) classes of econometric models by providing a detailed methodological review where they meet in terms of structure, interpretation, and estimation. We discuss the structure of connectivity (weight) matrices used by these models and its implications for estimation. To anchor our work within the dynamic literature on spillovers, we define a general yet measurable concept of spillovers. We formalize it analytically through the indirect effects used in the spatial literature and impulse responses used in the GVAR literature. Finally, we propose a practical step‐by‐step approach for applied researchers who need to account for the existence and strength of cross‐sectional dependence in the data. This approach aims to support the selection of the appropriate modeling and estimation method and of choices that represent empirical spillovers in a clear and interpretable form.
Highlights
Cross-sectional dependence has become a major research area in the econometrics literature
In these classes of models, spillover effects should be measured by impulse response functions from global vector autoregressive (GVAR)
The starting point for our exposition of where spatial and GVAR models meet in terms of structure is a cross-section of N units observed over T time periods
Summary
Cross-sectional dependence has become a major research area in the econometrics literature. The level of α = 0.75, separating sparse from dense connectivity matrices, helps to distinguish four general conditions related to the weight matrix W underpinning the consistency of OLS/ML/IV/GMM estimators of spatial econometric/GVAR models This finding speaks in favor of a more granular approach to cross-sectional dependence compared to the standard distinction in the literature between weak and strong cross-sectional dependence at α = 0.5. Fourth, in these classes of models, spillover effects should be measured by impulse response functions from GVARs (i.e. direct and indirect effects in spatial econometric models). Additional topics are discussed in appendices, including the concept of dominant units, the use of exogenous or pre-calibrated versus estimated connectivity matrices, and the model solution
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
