Structural Scenario Analysis and Stress Testing with Vector Autoregressions

In the context of linear vector autoregressions (VAR), conditional forecasts and “stress tests” are typically constructed by specifying the future path of one or more endogenous variables, while remaining silent about the underlying structural economic shocks that might have caused that path. However, in many cases researchers are interested in choosing which structural shock is driving the path of the conditioning variables, allowing the construction of a “structural scenario” which can be given an economic interpretation. We develop efficient algorithms to compute structural scenarios, and show how this procedure can lead to very different, and complementary, results to those of the traditional conditional forecasting exercises. Our methods allow to compute the full posterior distribution around these scenarios in the context of set and incompletely identified structural VARs, taking into account parameter and model uncertainty. Finally, we propose a metric to assess and compare the plausibility of alternative scenarios. We illustrate our methods by applying them to two examples: comparing alternative monetary policy options and stress testing bank profitability to an economic recession.