statsmodels.tsa.varma_process.VarmaPoly¶
-
class
statsmodels.tsa.varma_process.
VarmaPoly
(ar, ma=None)[source]¶ class to keep track of Varma polynomial format
Examples
- ar23 = np.array([[[ 1. , 0. ],
- [ 0. , 1. ]],
- [[-0.6, 0. ],
- [ 0.2, -0.6]],
- [[-0.1, 0. ],
- [ 0.1, -0.1]]])
- ma22 = np.array([[[ 1. , 0. ],
- [ 0. , 1. ]],
- [[ 0.4, 0. ],
- [ 0.2, 0.3]]])
Methods
getisinvertible
([a])check whether the auto-regressive lag-polynomial is stationary getisstationary
([a])check whether the auto-regressive lag-polynomial is stationary hstack
([a, name])stack lagpolynomial horizontally in 2d array hstackarma_minus1
()stack ar and lagpolynomial vertically in 2d array reduceform
(apoly)this assumes no exog, todo stacksquare
([a, name, orientation])stack lagpolynomial vertically in 2d square array with eye vstack
([a, name])stack lagpolynomial vertically in 2d array vstackarma_minus1
()stack ar and lagpolynomial vertically in 2d array