Factor Augmented Vector Autoregressions, Panel VARs, and Global VARs

Abstract

This chapter provides a thorough introduction to panel, global, and factor augmented vector autoregressive models. These models are typically used to capture interactions across units (i.e., countries) and variable types. Since including a large number of countries and/or variables increases the dimension of the models, all three approaches aim to decrease the dimensionality of the parameter space. After introducing each model, we briefly discuss key specification issues. A running toy example serves to highlight this point and outlines key differences across the different models. To illustrate the merits of the competing approaches, we perform a forecasting exercise and show that it pays off to introduce cross-sectional information in terms of forecasting key macroeconomic quantities.