Estimating GVAR Weight Matrices

49 Pages Posted: 7 Mar 2013

See all articles by Marco Gross

Marco Gross

International Monetary Fund (IMF); European Central Bank (ECB)

Date Written: March 2013

Abstract

This paper aims to illustrate how weight matrices 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 GDP and consumption expenditure price 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 that are traditionally employed in that context; 2) misspecified weights might bias the GVAR estimate and therefore distort its dynamics; 3) using estimated GVAR weights instead of trade-based ones (to the extent that they differ and the latter bias the global model estimates) shall 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.

Keywords: global macroeconometric modeling, models with panel data, forecasting and simulation

JEL Classification: C33, C53, C61, E17

Suggested Citation

Gross, Marco, Estimating GVAR Weight Matrices (March 2013). ECB Working Paper No. 1523, Available at SSRN: https://ssrn.com/abstract=2224041

Marco Gross (Contact Author)

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

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