| Abstract: |
This paper considers the problem of forecasting real and financial
macroeconomic variables across a large number of countries in the global
economy. To this end a global vector autoregressive (GVAR) model previously
estimated over the 1979Q1-2003Q4 period by Dees, de Mauro, Pesaran, and Smith
(2007), is used to generate out-of-sample one quarter and four quarters ahead
forecasts of real output, inflation, real equity prices, exchange rates and
interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134
variables from 26 regions made up of 33 countries covering about 90% of world
output. The forecasts are compared to typical benchmarks: univariate
autoregressive and random walk models. Building on the forecast combination
literature, the effects of model and estimation uncertainty on forecast
outcomes are examined by pooling forecasts obtained from different GVAR models
estimated over alternative sample periods. Given the size of the modeling
problem, and the heterogeneity of economies considered — industrialised,
emerging, and less developed countries — as well as the very real likelihood
of possibly multiple structural breaks, averaging forecasts across both models
and windows makes a significant difference. Indeed the double-averaged GVAR
forecasts performed better than the benchmark competitors, especially for
output, inflation and real equity prices. |