Small open economies and external shocks: an application of Bayesian global vector autoregression model

This study assesses the impact of external shocks on select small open economies (SOEs) using the Bayesian variant of the global vector autoregression model with time varying parameters and stochastic volatility. We account for the curse of dimensionality in the multi-country VAR system by implementing three different priors in the estimation of the parameters of the model: the Minnesota (M-N) prior of Doan–Litterman et al. (1984; Litterman 1986); the Normal-Gamma (N-G) prior of Park and Casella (Bayesian Anal 1:515–533, 2008); and the Stochastic Search Variable Selection (SSVS) prior of George and McCulloch (1995) as extended by Koop and Korobilis (2010, 2013). From our simulation results, we found that global economies of the USA, Western Europe and China are the major drivers of cyclical fluctuation in the SOEs. However, in spite of the perceived superior influence of China on the SOEs GDPs’ response to external shocks, we found no evidence to conclude that the influence is significantly greater than those exerted by the United States or Europe on the bloc’s economies.