Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/154166
Authors: 
Bańbura, Marta
Giannone, Domenico
Lenza, Michele
Year of Publication: 
2014
Series/Report no.: 
ECB Working Paper 1733
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 models that can be cast in a linear state space representation. We build large vector autoregressions (VARs) and a large dynamic factor model (DFM) for a quarterly data set of 26 euro area macroeconomic and financial indicators. 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.
Subjects: 
Bayesian shrinkage
conditional forecast
dynamic factor model
large cross-sections
vector autoregression
JEL: 
C11
C13
C33
C53
ISBN: 
978-92-899-1141-2
Document Type: 
Working Paper

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