Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/196132
Authors: 
Hauber, Philipp
Schumacher, Christian
Zhang, Jiachun
Year of Publication: 
2019
Series/Report no.: 
Bundesbank Discussion Paper No. 15/2019
Abstract: 
We provide a simulation smoother to a exible state-space model with lagged states and lagged dependent variables. Qian (2014) has introduced this state-space model and proposes a fast Kalman filter with time-varying state dimension in the presence of missing observations in the data. In this paper, we derive the corresponding Kalman smoother moments and propose an efficient simulation smoother, which relies on mean corrections for unconditional vectors. When applied to a factor model, the proposed simulation smoother for the states is efficient compared to other state-space models without lagged states and/or lagged dependent variables in terms of computing time.
Subjects: 
state-space model
missing observations
Kalman filter and smoother
simulation smoothing
factor model
JEL: 
C11
C32
C38
C63
C55
ISBN: 
978-3-95729-582-8
Document Type: 
Working Paper

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