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Evaluating a Global Vector Autoregression for Forecasting

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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 et al. (2009) generate and evaluate forecasts from a paradigm GVAR with 26 countries, based on Dées, di Mauro et al. (2007). The current paper empirically assesses the GVAR in Dées, di Mauro et al. (2007) 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.

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Acknowledgements

The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System. The authors are grateful to David Hendry, Jesper Lindé, Filippo di Mauro, Hashem Pesaran, Tara Rice, and Vanessa Smith for helpful discussions and comments; and additionally to Vanessa Smith for invaluable guidance in replicating estimation results from Dées, di Mauro, Pesaran, and Smith (2007). All numerical results were obtained using PcGive Version 13.10 and Autometrics Version 1.5e in OxMetrics 6.10: see Doornik and Hendry (2009) and Doornik (2009).

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Authors and Affiliations

  1. Division of International Finance, Federal Reserve Board, Washington, DC, 20551, USA

    Neil R. Ericsson & Erica L. Reisman

  2. Research Program on Forecasting, Department of Economics, The George Washington University, Washington, DC, 20052, USA

    Neil R. Ericsson

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  1. Neil R. Ericsson
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  2. Erica L. Reisman
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Correspondence to Neil R. Ericsson.

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Ericsson, N.R., Reisman, E.L. Evaluating a Global Vector Autoregression for Forecasting. Int Adv Econ Res 18, 247–258 (2012). https://doi.org/10.1007/s11294-012-9357-0

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