4.8.10.2.1. 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]]])
4.8.10.2.1.1. Methods¶
__init__ (ar[, ma]) |
|
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 |