About SsfPack
by Siem Jan Koopman, Neil Shephard and Jurgen A. Doornik
SsfPack is a suite of C routines for carrying out computations involving
the statistical analysis of univariate and multivariate models in state
space form. The fully implemented link we have made is to Ox 3.0 and
higher, the object-oriented matrix programming language of Doornik
(1998-2006). It is compatible with the latest
Ox 4.0.
SsfPack allows for a full range of different state space
forms: from a simple time-invariant model to a complicated multivariate
time-varying model. Functions are provided to put standard models such
as ARIMA, Unobserved components, regressions and cubic spline models into
state space form. Basic functions
are available for filtering, moment smoothing and simulation smoothing.
Ready-to-use functions are provided for standard tasks such as
likelihood evaluation, forecasting and signal extraction. SsfPack can be
easily used for implementing, fitting and analysing Gaussian models
relevant to many areas of econometrics and statistics. Furthermore it
provides all relevant tools for the treatment of non-Gaussian and
nonlinear state space models. In particular, tools are available to implement
simulation based estimation methods such as importance sampling and
Markov chain Monte Carlo (MCMC) methods.
About the authors
Siem Jan Koopman is Professor at
Free University Amsterdam,
Economics Faculty,
Department of Econometrics.
De Boelelaan 1105, NL-1081 HV Amsterdam, The Netherlands.
Neil Shephard
is Professor of Economics at
Oxford University and an Official Fellow at
Nuffield College, Oxford OX1 1NF, UK.
Jurgen A Doornik
is a Research Fellow at
Nuffield College,
Oxford OX1 1NF, UK.
For more information on Ox and GiveWin but also on PcGive
and PcFiml, just visit the
Doornik work page.
About the SsfPack workpage
The SsfPack workpage at http://www.ssfpack.com
is designed and maintained by S.J. Koopman and K.M. Lee. Please send questions,
comments and remarks about the website to
klee.feweb@gmail.com,
with "www.ssfpack.com" in the subject line.
List of SsfPack functions
Models in state space form
AddSsfReg() | adds regression effects to time-invariant state space
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GetSsfArma() | puts ARMA model in state space
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GetSsfReg() | puts regression model in state space
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GetSsfSpline() | puts nonparametric cubic spline model in state space
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GetSsfStsm() | puts unobserved components time series model in state space
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SsfCombine() | combines system matrices of two models
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SsfCombineSym() | combines symmetric system matrices of two models
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General state space algorithms (for univariate and multivariate models)
KalmanFil() | returns output of the Kalman filter
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KalmanSmo() | returns output of the basic smoothing algorithm
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SimSmoDraw() | returns a sample from the simulation smoother
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SimSmoWgt() | returns covariance output of the simulation smoother
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Ready-to-use functions (for univariate and multivariate models)
SsfCondDens() | returns mean or a draw from the conditional density
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SsfLik() | returns log-likelihood function
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SsfLikConc() | returns profile log-likelihood function
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SsfLikSco() | returns score vector
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SsfMomentEst() | returns output from prediction, forecasting and smoothing
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SsfRecursion() | returns output of the state space recursion
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