Assessing Global Vector Autoregressions for Forecasting

• Activity Number: 521
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Neil R. Ericsson*+
• Companies: Federal Reserve Board
• Keywords: cointegration ; forecasting ; impulse indicator saturation ; model evaluation ; parameter constancy ; VAR

Abstract

Global vector autoregressions (GVARs) have several attractive features: multiple potential channels for the 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 the GVAR in Dees, di Mauro, Pesaran, and Smith (2007, op. cit.) with impulse indicator saturation (IIS)---a new generic procedure for evaluating parameter constancy, which is a central element in model-based forecasting. The empirical results indicate substantial room for an improved, more robust specification of that GVAR. Some tests are suggestive of how to achieve such improvements.