Evaluating a Global Vector Autoregression for Forecasting

Neil Ericsson and Erica L. Reisman ()
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Erica L. Reisman: Board of Governors of the Federal Reserve System

No 2012-006, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting

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

Keywords: cointegration; error correction; forecasting; GVAR; impulse indicator saturation; model design; model evaluation; model selection; parameter constancy; VAR (search for similar items in EconPapers)
JEL-codes: C32 F41 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2012-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-opm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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https://www2.gwu.edu/~forcpgm/2012-006.pdf First version, 2012 (application/pdf)

Related works:
Journal Article: Evaluating a Global Vector Autoregression for Forecasting (2012)
Working Paper: Evaluating a global vector autoregression for forecasting (2012)
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