Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/154166 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
ECB Working Paper No. 1733
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Bayesian shrinkage
conditional forecast
dynamic factor model
large cross-sections
vector autoregression
JEL: 
C11
C13
C33
C53
ISBN: 
978-92-899-1141-2
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
896.31 kB





Publikationen in EconStor sind urheberrechtlich geschützt.