Estimating GVAR weight matrices

Marco Gross

Spatial Economic Analysis, 2019, vol. 14, issue 2, 219-240

Abstract: The objective of this paper is to illustrate how the weights that are needed to construct foreign variable vectors in global vector autoregressive (GVAR) models can be estimated jointly with the GVAR’s parameters. An application to real gross domestic product (GDP) growth and inflation as well as a controlled Monte Carlo simulation serve to highlight that (1) in the application at hand, the estimated weights differ for some countries significantly from trade-based ones; (2) misspecified weights can bias the GVAR and, hence, distort the impulse responses; and (3) using estimated weights instead of trade-based ones can enhance the out-of-sample forecast performance of the GVAR. Devising a method for estimating GVAR weights is particularly useful for contexts in which it is not obvious how weights could otherwise be constructed from data.

Date: 2019
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DOI: 10.1080/17421772.2019.1556800

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