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


Last update: Jul 16, 2024