Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections

This paper describes an algorithm to compute the distribution of conditional forecasts,i.e. projections of a set of variables of interest on future paths of some othervariables, in dynamic systems. The algorithm is based on Kalman filtering methods andis computationally viable for large vector autoregressions (VAR) and dynamic factormodels (DFM). For a quarterly data set of 26 euro area macroeconomic and financialindicators, we show that both approaches deliver similar forecasts and scenario assessments.In addition, conditional forecasts shed light on the stability of the dynamicrelationships in the euro area during the recent episodes of financial turmoil and indicatethat only a small number of sources drive the bulk of the fluctuations in the euroarea economy.