Statistical algorithms for models in state space using SsfPack 2. 2. The authors discuss and document 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 they 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 easily used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics and statistics. Some Gaussian illustrations are given.

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  1. Neale, Michael C.; Hunter, Michael D.; Pritikin, Joshua N.; Zahery, Mahsa; Brick, Timothy R.; Kirkpatrick, Robert M.; Estabrook, Ryne; Bates, Timothy C.; Maes, Hermine H.; Boker, Steven M.: OpenMX 2.0: extended structural equation and statistical modeling (2016)
  2. Tripodis, Yorghos; Neerchal, Nagaraj K.: Estimation of missing values in linear models (2014)
  3. Bellini, Tiziano; Riani, Marco: Robust analysis of default intensity (2012)
  4. Brinch, Christian N.: Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling (2012)
  5. Dordonnat, Virginie; Koopman, Siem Jan; Ooms, Marius: Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling (2012)
  6. Hautsch, Nikolaus; Yang, Fuyu: Bayesian inference in a stochastic volatility Nelson-Siegel model (2012)
  7. Krieg, Sabine; den Brakel, Jan A.Van: Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends (2012)
  8. Raggi, Davide; Bordignon, Silvano: Long memory and nonlinearities in realized volatility: a Markov switching approach (2012)
  9. Ruiz-Cárdenas, Ramiro; Krainski, Elias T.; Rue, Håvard: Direct fitting of dynamic models using integrated nested Laplace approximations -- INLA (2012)
  10. Birrell, Carole L.; Steel, David G.; Lin, Yan-Xia: Seasonal adjustment of an aggregate series using univariate and multivariate basic structural models (2011)
  11. Proietti, Tommaso; Frale, Cecilia: New proposals for the quantification of qualitative survey data (2011)
  12. Hindrayanto, Irma; Koopman, Siem Jan; Ooms, Marius: Exact maximum likelihood estimation for non-stationary periodic time series models (2010)
  13. McElroy, Tucker; Wildi, Marc: Signal extraction revision variances as a goodness-of-fit measure (2010)
  14. van den Brakel, Jan; Roels, Joeri: Intervention analysis with state-space models to estimate discontinuities due to a survey redesign (2010)
  15. Harvey, Andrew C.; Monache, Davide Delle: Computing the mean square error of unobserved components extracted by misspecified time series models (2009)
  16. Jungbacker, Borus; Koopman, Siem Jan: Parameter estimation and practical aspects of modeling stochastic volatility (2009)
  17. Tripodis, Yorghos; Buonaccorsi, John P.: Prediction and forecasting in linear models with measurement error (2009)
  18. Bowsher, Clive G.; Meeks, Roland: The dynamics of economic functions: modeling and forecasting the yield curve (2008)
  19. McElroy, Tucker: Matrix formulas for nonstationary ARIMA signal extraction (2008)
  20. Harvey, Andrew C.; Trimbur, Thomas M.; Van Dijk, Herman K.: Trends and cycles in economic time series: a Bayesian approach (2007)

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