Working Paper: CEPR ID: DP9931
Authors: Marta Banbura; Domenico Giannone; Michele Lenza
Abstract: 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 other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large vector autoregressions (VAR) and dynamic factor models (DFM). For a quarterly data set of 26 euro area macroeconomic and financial indicators, we show that both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy.
Keywords: Bayesian Shrinkage; Conditional Forecast; Dynamic Factor Model; Large Cross-Sections; Vector Autoregression
JEL Codes: C11; C13; C33; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| real GDP (E20) | economic performance in the euro area (E66) |
| consumer prices (P22) | economic performance in the euro area (E66) |
| short-term interest rates (E43) | economic performance in the euro area (E66) |
| conditional forecasts (C53) | economic performance in the euro area (E66) |
| stability of economic relationships (F41) | accuracy of conditional forecasts (C53) |