Spatiotemporal impact of trade policy variables on Asian manufacturing hubs: Bayesian global vector autoregression model

A novel spatiotemporal framework using diverse econometric approaches is proposed to analyse relationships among economy-wide variables in varying market conditions. Employing vector autoregression and Granger causality, we explore trade policy effects on emerging manufacturing hubs in China, India, Malaysia, Singapore, and Vietnam. A Bayesian global vector autoregression (BGVAR) model assesses the interaction of cross-unit and performs unconditional and conditional forecasts. Utilising time-series data, this study reveals multi-way cointegration and dynamic connectedness relationships among economy-wide variables. This innovative framework enhances investment decisions and policymaking through a data-driven approach, contributing to the development of a unique conceptual spatiotemporal framework for causality, co-integration, and dynamic connectedness. The results are consistent with existing economic theory. Currency devaluations lead to inflation which forces governments to slow down the economy amid its negative effect on employment. Similarly, fluctuating levels of imports/exports alter the flows of foreign currency enforcing policymakers to react to preserve the exchange rates around their real values.