Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/105153
Authors: 
Chudik, Alexander
Grossman, Valerie
Pesaran, M. Hashem
Year of Publication: 
2014
Series/Report no.: 
CESifo Working Paper 5100
Abstract: 
This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of a strong unobserved common factor can lead to an undeter-mined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability to the infeasible optimal forecasts obtained from a factor-augmented high-dimensional VAR model. The small sample properties of the proposed solution are investigated by Monte Carlo experiments as well as empirically. In the empirical part, we investigate the value of the information content of Purchasing Managers Indices (PMIs) for forecasting global (48 countries) growth, and compare forecasts from AugGVAR models with a number of data-rich forecasting methods, including Lasso, Ridge, partial least squares and factor-based methods. It is found that (a) regardless of the forecasting methods considered, PMIs are useful for nowcasting, but their value added diminishes quite rapidly with the forecast horizon, and (b) AugGVAR forecasts do as well as other data-rich forecasting techniques for short horizons, and tend to do better for longer forecast horizons.
Subjects: 
global VARs
high-dimensional VARs
augmented GVAR
forecasting
nowcasting
data-rich methods
GDP and PMIs
JEL: 
C53
E37
Document Type: 
Working Paper

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