#Eviews 10 gvar driver
They find that direct trade still appears to be the most important driver of regional interdependencies and that financial linkages have a comparably minor effect. Forbes and Chinn ( 2004) come to a mixed conclusion concerning the explanatory power of financial cross-country linkages. This logic also applies to other investors receiving margin calls (Calvo 1999). For example, banks that incur losses in one country could see themselves forced to sell assets in other countries in order to fulfil their capital requirements. Van Rijckeghem and Weder ( 2001) argue that multiple financial mechanisms can cause cross-border spillovers. However, the recent financial crisis has demonstrated that financial linkages appear to be an important determinant of macroeconomic spillovers.Īs such, financial linkages have also been put forward as an important channel of international shock transmission. Given the importance of trade as a major driver of business-cycle co-movement (Baxter and Kouparitsas 2005), basing an empirical model on trade weights is certainly a reasonable choice. The standard method of averaging foreign variables in GVAR specifications is based on trade weights and in most applications on the share of bilateral trade between two countries in relation to the total trade volume of each one of those countries. The most common approach found in the literature using such a technique is given by global vector autoregressive (or GVAR) specifications (see for example Pesaran et al. An obvious choice is to use weighted averages of variables as this allows capturing information of other regions in each single country model, much like in a spatial econometric model. One way to limit the parameter space in econometric modelling exercises aimed at assessing cross-country linkages is to use combinations of variables in surrounding economies as regressors in order to model the interdependencies among markets. Modelling the complex linkages of a multitude of countries quickly results in econometric specifications with many more variables and parameters than observations, thus making models which are not sparsely parametrized impracticable. In the attempt to analyse and understand the dynamics that drive an interconnected global economy, researchers have developed a range of econometric approaches. In an increasingly globalized world, interactions between economies are becoming more important as business cycles harmonise among major economies and shocks spread quickly across the globe. In recent years, exploring interdependencies across countries and markets has become one of the major fields of research in economics.