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.

References in zbMATH (referenced in 66 articles , 1 standard article )

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  1. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  2. dos Santos, Thiago R.; Franco, Glaura C.: Bootstrap for correcting the mean square error of prediction and smoothed estimates in structural models (2019)
  3. Marco Villegas; Diego Pedregal: SSpace: A Toolbox for State Space Modeling (2018) not zbMATH
  4. Zirogiannis, Nikolaos; Tripodis, Yorghos: Dynamic factor analysis for short panels: estimating performance trajectories for water utilities (2018)
  5. 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)
  6. Borovkova, Svetlana; Mahakena, Diego: News, volatility and jumps: the case of natural gas futures (2015)
  7. Victor Gómez: SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models (2015) not zbMATH
  8. Bos, Charles S.; Koopman, Siem Jan; Ooms, Marius: Long memory with stochastic variance model: a recursive analysis for US inflation (2014)
  9. Tripodis, Yorghos; Neerchal, Nagaraj K.: Estimation of missing values in linear models (2014)
  10. Bellini, Tiziano; Riani, Marco: Robust analysis of default intensity (2012)
  11. Brinch, Christian N.: Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling (2012)
  12. Dordonnat, Virginie; Koopman, Siem Jan; Ooms, Marius: Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling (2012)
  13. Hautsch, Nikolaus; Yang, Fuyu: Bayesian inference in a stochastic volatility Nelson-Siegel model (2012)
  14. 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)
  15. Raggi, Davide; Bordignon, Silvano: Long memory and nonlinearities in realized volatility: a Markov switching approach (2012)
  16. Ruiz-Cárdenas, Ramiro; Krainski, Elias T.; Rue, Håvard: Direct fitting of dynamic models using integrated nested Laplace approximations -- INLA (2012)
  17. Birrell, Carole L.; Steel, David G.; Lin, Yan-Xia: Seasonal adjustment of an aggregate series using univariate and multivariate basic structural models (2011)
  18. Charles Bos: A Bayesian Analysis of Unobserved Component Models Using Ox (2011) not zbMATH
  19. Eric Zivot: State Space Modeling Using SsfPack in S+FinMetrics 3.0 (2011) not zbMATH
  20. Fernando Tusell: Kalman Filtering in R (2011) not zbMATH

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