@ARTICLE{18357586_1999,
author = {Koopman, Siem Jan and Shephard , Neil and Doornik, Jurgen A.},
keywords = {математическая модель, математическая статистика, обзор программного обеспечения, программа, программное обеспечение, статистическая модель},
title = {Statistical algorithms for models in state space form using SsfPack
2.2. },
journal = {Econometrics Journal},
year = {1999},
month = {},
volume = {2},
number = {1},
pages = {107-160},
url = {http://ecsocman.hse.ru/text/18357586/},
publisher = {},
language = {ru},
abstract = {The paper discusses and documents the algorithms of SsfPack 2.2.
SsfPack is a suite of C routines for carrying out computations
involving the statistical analysis of univariate models in state
space form. The emphasis is on documenting the link we have made to
the Ox computing environment. SsfPack allows for a full range of
different state space forms: from a simple time-invariant model to a
complicated time-varying model. Functions can be used which put
standart models such as ARMA and cubic spline models in state space
form. Basic functions are available for filtering, moment smoothing
and simulation smoothing. Ready-to-use functions are provided for
standart tasks such as likelihood evaluation, forecasting and signal
extraction. They shows that SsfPack can be easily used for
implementing, fitting and analysing Gaussian models relevant to many
areas of econometrics and statistics. Some Gaussian illustrations are
given. },
annote = {The paper discusses and documents the algorithms of SsfPack 2.2.
SsfPack is a suite of C routines for carrying out computations
involving the statistical analysis of univariate models in state
space form. The emphasis is on documenting the link we have made to
the Ox computing environment. SsfPack allows for a full range of
different state space forms: from a simple time-invariant model to a
complicated time-varying model. Functions can be used which put
standart models such as ARMA and cubic spline models in state space
form. Basic functions are available for filtering, moment smoothing
and simulation smoothing. Ready-to-use functions are provided for
standart tasks such as likelihood evaluation, forecasting and signal
extraction. They shows that SsfPack can be easily used for
implementing, fitting and analysing Gaussian models relevant to many
areas of econometrics and statistics. Some Gaussian illustrations are
given. }
}