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A FLEXIBLE STATE SPACE MODEL AND ITS APPLICATIONS
Author(s) -
Qian Hang
Publication year - 2014
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12051
Subject(s) - observability , state space , mathematics , autoregressive model , stochastic volatility , dimension (graph theory) , state space representation , state (computer science) , econometrics , volatility (finance) , statistical physics , algorithm , statistics , pure mathematics , physics
The standard state space model treats observations as imprecise measurement of the Markovian states. Our flexible model handles the states and observations symmetrically, which are simultaneously determined by past observations and up to first‐lagged states. The only distinction between the states and observations is the observability. When it is applied to the autoregressive moving average, dynamic factor and stochastic volatility models, the state space form is both parsimonious and intuitive, for low‐dimension states are constructed simply by stacking all the relevant but unobserved components in the structural model.