A Flexible State Space Model and its Applications
AbstractThe standard state space model (SSM) treats observations as imprecise measures of the Markov latent states. Our flexible SSM treats the states and observables symmetrically, which are simultaneously determined by historical observations and up to first-lagged states. The only distinction between the states and observables is that the former are latent while the latter have data. Despite the conceptual difference, the two SSMs share the same Kalman filter. However, when the flexible SSM is applied to the ARMA model, mixed frequency regression and the dynamic factor model with missing data, the state vector is not only parsimonious but also intuitive in that low-dimension states are constructed simply by stacking all the relevant but unobserved variables in the structural model.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 38455.
Date of creation: Apr 2012
Date of revision:
State Space Model; Kalman Filter; ARMA; Mixed Frequency; Factor Model;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-08 (All new papers)
- NEP-ECM-2012-05-08 (Econometrics)
- NEP-ETS-2012-05-08 (Econometric Time Series)
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