SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models. This article discusses and describes SSMMATLAB, a set of programs written by the author in MATLAB for the statistical analysis of state space models. The state space model considered is very general. It may have univariate or multivariate observations, time-varying system matrices, exogenous inputs, regression effects, incompletely specified initial conditions, such as those that arise with cointegrated VARMA models, and missing values. There are functions to put frequently used models, such as multiplicative VARMA models, VARMAX models in echelon form, cointegrated VARMA models, and univariate structural or ARIMA model-based unobserved components models, into state space form. There are also functions to implement the Hillmer-Tiao canonical decomposition and the smooth trend and cycle estimation proposed by Gez (2001). Once the model is in state space form, other functions can be used for likelihood evaluation, model estimation, forecasting and smoothing. A set of examples is presented in the SSMMATLAB manual to illustrate the use of these functions.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Gómez, Víctor: Linear time series with MATLAB and OCTAVE (2019)
- Pedregal, Diego J.; Villegas, Marco A.; Villegas, Diego A.; Trapero, Juan R.: Time series modeling with Matlab: the SSpace toolbox (2019)
- Marco Villegas; Diego Pedregal: SSpace: A Toolbox for State Space Modeling (2018) not zbMATH
- Gómez, Víctor: Multivariate time series with linear state space structure (2016)
- Victor Gómez: SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models (2015) not zbMATH