Statistical algorithms for models in state space using SsfPack 2.2

Abstract

Statistical Algorithms for Models in State Space Using SsfPack 2.2Koopman, S.J.M.; Shephard, N.; Doornik, J.A. Publication date: 1998Link to publication Citation for published version (APA):Koopman, S. J. M., Shephard, N., & Doornik, J. A. (1998). Statistical Algorithms for Models in State Space Using SsfPack 2.2. (CentER Discussion Paper; Vol. 1998-141). Tilburg: Econometrics. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. 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 h a v e made to the Ox computing environment. SsfPack allows for a full range of di erent 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 ARIMA and cubic spline models in state space form. Basic functions are available for ltering, 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 easily used for implementing, tting and analysing Gaussian models relevant t o many areas of econometrics and statistics. Some Gaussian illustrations are given.

Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling

“…E.g. the formula for asymptotic variance of simulated maximum likelihood estimators is used for assessments of stochastic variability in software such as SSF-pack (Koopman et al, 1999) or AD model builder (Skaug and Fournier, 2006). However, the spelling out of the components of W in equation (6) is to my knowledge new.…”

The inflation and inflation uncertainty relationship for Turkey: a dynamic framework

“…The parameters of this model are estimated by exact maximum likelihood methods, using Monte Carlo importance sampling techniques and using programs written in Doornik (1998) Ox language, using SsfPack by Koopman et al (1999). 4…”

A methodology to filter time series: application to minute-by-minute electric load series

“…Thus, we decided to use the smoothing cubic spline, previously applied to the interpolation of time series by Gordon (1996) and to the handling of missing values in Koopman (1991) and Koopman et al (1998).…”

“…The Smoothing Cubic Spline (SCS) is a non-parametric techniques that has been applied in interpolation problems in time series (see Ferreiro, 1987;and Koopman et al, 1998) with good results. This method has been used in some packages such as the SsfPack (Koopman et al, 1998b).…”

“…This method has been used in some packages such as the SsfPack (Koopman et al, 1998b). However, the SCS is not well-behaved for series with a considerable number of consecutive missing values.…”