is a suite of C routines for carrying out computations involving the
statistical analysis of time series models in state space form. SsfPack
provides functions for likelihood evaluation and signal extraction of
arbitrary user specified linear Gaussian state space models, allowing
the estimation of models ranging from simple time-invariant univariate
forms to complicated time-varying multivariate specifications. Basic
functions are available for prediction, moment smoothing and simulation
smoothing. Additionally, functions are provided which put standard
models such as autoregressive moving average (ARMA), unobserved
components (UC) and cubic spline models in state space form.
The functions from SsfPack can be easily used for implementing, fitting
and analysing linear Gaussian models relevant to many areas of
econometrics, statistics and time series analysis. Further, SsfPack
provides tools for estimating many non-Gaussian and nonlinear models
using implement simulation based estimation methods such as importance
sampling and Markov chain Monte Carlo (MCMC) methods.
SsfPack is primarily developed as a module for the object-oriented
matrix programming language Ox. The library is written in C, which
greatly improves execution speed compared to a direct Ox implementation.
A free version of SsfPack for academic research and teaching purposes
can be downloaded from this website.