A flexible state-space model with lagged states and lagged dependent variables: Simulation smoothing

Philipp Hauber, Christian Schumacher and Jiachun Zhang

No 15/2019, Discussion Papers from Deutsche Bundesbank

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.

Keywords: state-space model; missing observations; Kalman filter and smoother; simulation smoothing; factor model (search for similar items in EconPapers)
JEL-codes: C11 C32 C38 C55 C63 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-bec, nep-cmp, nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdps:152019

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