GAUSS State-Space Modeling (SSLIB)
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Description
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This package provides tools for estimating and evaluating time-invariant state-space models. In addition to providing the tools for custom state space models, it provides pre-built functions for state-space modeling of:
* ARIMA packages
* SARIMA packages
Installation
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The GAUSS State Space library can be installed and updated directly in GAUSS using the `GAUSS Package Manager `_. It requires a working copy of GAUSS 22+.
For more information on installing the GAUSS Package Manager please see our video blog, `Installing the GAUSS Package Manager `_.
Dependencies
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This library requires the installation of the following GAUSS libraries:
* `Time Series MT `_
* `Constrained Maximum Likelihood MT `_
Usage
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Estimating custom state-space models using the `sslib` library requires:
* Loading data
* Setting up the state-space representation
* Calling the estimation function :func:`ssFit`
See our `complete guide `_ to getting started with state-space estimation in GAUSS for a full example.
Commands
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Estimating custom models
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:func:`ssFit` Estimates parameters of a state-space model using Kalman filtering and maximum likelihood estimation.
:func:`ssIRF` Computes the impulse response functions based on the final estimated parameters.
:func:`ssPredict` Computes in sample predictions or out-of-sample forecasts based on the final estimated parameters.
:func:`ssKalmanSmooth` Performs the Rauch-Tung-Striebel backward recursion smoother for state variables and state covariances.
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Pre-built models
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:func:`ssARIMA` Estimates parameters of a state-space model ARIMA(p, d, q) model using Kalman filtering and maximum likelihood estimation.
:func:`ssSARIMA` Estimates parameters of a state-space model SARIMA(p, d, q)(p_s, d_s, q_s) model using Kalman filtering and maximum likelihood estimation.
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Model evaluation
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:func:`ssgetAIC` Computes Akaike's information criterion from loglikelihood.
:func:`ssgetAICC` Computes the corrected Akaike's information criterion from loglikelihood.
:func:`ssgetBIC` Computes the Schwarz’ Bayesian information criterion from loglikelihood.
:func:`ssgetHQIC` Computes the Hannan–Quinn information criterion from loglikelihood.
:func:`ssHeteroskedasticityTest` Tests the null hypothesis of no heteroskedasticity by comparing the sum-of-squares of the first third of the sample to the sum-of-squares of last third of the sample. Analogous to a Goldfeld-Quandt test.
:func:`ssJarqueBera` Performs the Jarque-Bera goodness-of-fit test on model residuals.
:func:`ssLjungBox` Computes the Ljung-Box test for autocorrelation.
:func:`ssSkewness` Computes the sample skewness.
:func:`ssKurtosis` Compute the sample kurtosis.
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.. toctree::
:maxdepth: 1
:hidden:
:caption: State-space modeling
getting-started
ssarima
sscontrolcreate
ssfit
ssgetaic
ssgetaicc
ssgetbic
ssgethqic
ssheteroskedasticitytest
ssirf
ssjarquebera
sskalmansmooth
sskurtosis
ssljungbox
sspredict
sssarima
ssskewness