Evaluating forecast performance of global vector autoregressive model

Abstract

This paper aims to investigate how GVAR (Global Vector Autoregressive) fares against other macro models. For the forecasting exercise, the ability is compared between a generic AR (Autoregressive) model with GVAR ex-ante and GVAR-ex post forecasts. It is easy to see that certain properties are similar among the models such as the long run appears to be unaffected by a monetary shock or that the GDP is negatively affected by it. However, there are also a lot of discrepancies in the short run, particularly in the first 4 quarters. From this, we can conclude that the GVAR model fares best in forecasting that it explicitly allows error correction mechanisms among country models. The paper concludes that the GVAR model is quite adaptable in terms of allowing the data to dictate the short run but also relying on more theory-led identification for the long run.