Improving Global Vector Autoregressions

Global vector autoregressions (GVARs) have several attractive features: a standardized economically appealing choice of variables for each country or region examined, a systematic treatment of long-run properties through cointegration analysis, and flexible dynamic specification through vector error correction modeling. The current paper re-examines the theoretical and empirical underpinnings for GVARs, focusing on exogeneity assumptions, parameter constancy, and data aggregation. This paper proposes refinements in these areas, with the aim of achieving an even more robust approach to GVAR modeling. The GVAR in Dées, di Mauro, Pesaran, and Smith (2007) is used to illustrate these refinements.

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