期刊名称：Euro Area Balance of Payments and International Investment Position Statistics
印刷版ISSN：1830-3420
电子版ISSN：1830-3439
出版年度：2014
出版社：European Central Bank
摘要：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 methodsand is computationally viable for large models that can be cast in a linear state spacerepresentation. We build large vector autoregressions (VARs) and a large dynamic factormodel (DFM) for a quarterly data set of 26 euro area macroeconomic and financialindicators. Both approaches deliver similar forecasts and scenario assessments. In addition,conditional forecasts shed light on the stability of the dynamic relationships in theeuro area during the recent episodes of financial turmoil and indicate that only a smallnumber of sources drive the bulk of the fluctuations in the euro area economy.
关键词：Vector Autoregression; Bayesian Shrinkage; Dynamic Factor Model; Conditional;Forecast; Large Cross-Sections
