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Bayesian SAR model with stochastic volatility and multiple time-varying weights. (arXiv:2310.17473v1 [stat.AP])

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A novel spatial autoregressive model for panel data is introduced, which
incorporates multilayer networks and accounts for time-varying relationships.
Moreover, the proposed approach allows the structural variance to evolve
smoothly over time and enables the analysis of shock propagation in terms of
time-varying spillover effects. The framework is applied to analyse the
dynamics of international relationships among the G7 economies and their impact
on stock market returns and volatilities. The findings underscore the
substantial impact of cooperative interactions and highlight discernible
disparities in network exposure across G7 nations, along with nuanced patterns
in direct and indirect spillover effects.

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