statsmodels.tsa.varma_process.VarmaPoly#
- class statsmodels.tsa.varma_process.VarmaPoly(ar, ma=None)[source]#
Class to keep track of Varma polynomial format
Methods
getisinvertible([a])Check whether the moving-average lag-polynomial is invertible
getisstationary([a])Check whether the auto-regressive lag-polynomial is stationary
hstack([a, name])Stack lagpolynomial horizontally in 2d array
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
Stack ar and lagpolynomial vertically in 2d array
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 moving-average lag-polynomial is invertible
getisstationary([a])Check whether the auto-regressive lag-polynomial is stationary
hstack([a, name])Stack lagpolynomial horizontally in 2d array
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
Stack ar and lagpolynomial vertically in 2d array