Quasifiltering for time-series modeling
MPRA Paper from University Library of Munich, Germany
In the paper a method for constructing new varieties of time-series models is proposed. The idea is to start from an unobserved components model in a state-space form and use it as an inspiration for development of another time-series model, in which time-varying underlying variables are directly observed. The goal is to replace a state-space model with an intractable likelihood function by another model, for which the likelihood function can be written in a closed form. If state transition equation of the parent state-space model is linear Gaussian, then the resulting model would belong to the class of score driven model (aka GAS, DCS).
Keywords: time-series model; state-space model; score driven model (search for similar items in EconPapers)
JEL-codes: C22 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:66453
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