Statistical algorithms for models in state space using SsfPack 2.2

Siem Jan Koopman, Neil Shephard () and Jurgen Doornik

Econometrics Journal, 1999, vol. 2, issue 1, 107-160

Abstract: This 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 and multivariate 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 standard 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 standard tasks such as likelihood evaluation, forecasting and signal extraction. We show that SsfPack can be eas-ily used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics and statistics. Some Gaussian illustrations are given.

Keywords: Kalman filtering and smoothing; Markov chain Monte Carlo; Ox; Simulation smoother; State space. (search for similar items in EconPapers)
Date: 1999
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Related works:
Working Paper: Statistical Algorithms for Models in State Space Using SsfPack 2.2 (1998)
Working Paper: Statistical Algorithms for Models in State Space Using SsfPack 2.2 (1998)
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