Estimating GVAR weight matrices. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Gross, Marco
• Is Part Of: Spatial economic analysis. Volume 14:Issue 2(2019)
• Page Start: 219
• Page End: 240
• Publication Date: 2019-04-03
• Subjects: spatio-temporal panel models -- global macro-econometric modelling -- forecasting and simulation; C33 -- C53 -- C61 -- E17; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2019.1556800
• Languages: English
• ISSNs: 1742-1772

Abstract

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.