tsa.statespace.tools.unconstrain_stationary_multivariate()
statsmodels.tsa.statespace.tools.unconstrain_stationary_multivariate
-
statsmodels.tsa.statespace.tools.unconstrain_stationary_multivariate(constrained, error_variance)
[source] -
Transform constrained parameters used in likelihood evaluation to unconstrained parameters used by the optimizer
Parameters: constrained : array or list
Constrained parameters of, e.g., an autoregressive or moving average component, to be transformed to arbitrary parameters used by the optimizer. If a list, should be a list of length
order
, where each element is an array sizedk_endog
xk_endog
. If an array, should be the coefficient matrices horizontally concatenated and sizedk_endog
xk_endog * order
.error_variance : array
The variance / covariance matrix of the error term. Should be sized
k_endog
xk_endog
. This is used as input in the algorithm even if is not transformed by it (whentransform_variance
is False).Returns: unconstrained : array
Unconstrained parameters used by the optimizer, to be transformed to stationary coefficients of, e.g., an autoregressive or moving average component. Will match the type of the passed
constrained
variable (so if a list was passed, a list will be returned).Notes
Uses the list representation internally, even if an array is passed.
References
[R103] Ansley, Craig F., and Robert Kohn. 1986. “A Note on Reparameterizing a Vector Autoregressive Moving Average Model to Enforce Stationarity.” Journal of Statistical Computation and Simulation 24 (2): 99-106.
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.tools.unconstrain_stationary_multivariate.html