A Flexible State-Space Model with Lagged States and Lagged Dependent Variables: Simulation Smoothing

25 Pages Posted: 15 Apr 2019

See all articles by Philipp Hauber

Philipp Hauber

Kiel Institute for the World Economy

Christian Schumacher

Deutsche Bundesbank

jiachun zhang

affiliation not provided to SSRN

Date Written: 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.

Keywords: state-space model, missing observations, Kalman filter and smoother, simulation smoothing, factor model

JEL Classification: C11, C32, C38, C63, C55

Suggested Citation

Hauber, Philipp and Schumacher, Christian and zhang, jiachun, A Flexible State-Space Model with Lagged States and Lagged Dependent Variables: Simulation Smoothing (2019). Deutsche Bundesbank Discussion Paper No. 15/2019, Available at SSRN: https://ssrn.com/abstract=3391555 or http://dx.doi.org/10.2139/ssrn.3391555

Philipp Hauber

Kiel Institute for the World Economy

P.O. Box 4309
Kiel, Schleswig-Hosltein D-24100
Germany

Christian Schumacher (Contact Author)

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

Jiachun Zhang

affiliation not provided to SSRN

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