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Estimating GVAR weight matrices

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  • Marco Gross

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.

Suggested Citation

  • Marco Gross, 2019. "Estimating GVAR weight matrices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(2), pages 219-240, April.
    • Handle: RePEc:taf:specan:v:14:y:2019:i:2:p:219-240
    DOI: 10.1080/17421772.2019.1556800
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    Cited by:

    1. Candelon, Bertrand & Luisi, Angelo & Roccazzella, Francesco, 2022. "Fragmentation in the European Monetary Union: Is it really over?," Journal of International Money and Finance, Elsevier, vol. 122(C).
    2. Peter Dolton & Arno Hantzsche, 2024. "Follow the leader? The long‐run interaction between public and private sector wage growth in the UK," Economica, London School of Economics and Political Science, vol. 91(363), pages 837-879, July.
    3. Razieh Zahedi & Asghar Shahmoradi & Ali Taiebnia, 2022. "The ever-evolving trade pattern: a global VAR approach," Empirical Economics, Springer, vol. 63(3), pages 1193-1218, September.

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