Assessing global vector autoregressions for forecasting

Global vector autoregressions (GVARs) have several attractive features: multiple potential channels for international transmission of macroeconomic and financial shocks, a standardized economically appealing choice of variables for each country or region examined, systematic treatment of long-run properties through cointegration analysis, and flexible dynamic specification through vector error correction modeling. Pesaran, Schuermann, and Smith (2009, International Journal of Forecasting) generate and evaluate forecasts from a paradigm GVAR with 26 countries, based on Dees, di Mauro, Pesaran, and Smith (2007, Journal of Applied Econometrics). The current paper empirically assesses that GVAR with impulse indicator saturation (IIS)—a new generic procedure for evaluating parameter constancy, which is a central element in model-based forecasting.  The results indicate substantial room for an improved, more robust specification of that GVAR, with some tests suggestive of how to achieve such improvements.