statsmodels.tsa.varma_process.VarmaPoly

class statsmodels.tsa.varma_process.VarmaPoly(ar, ma=None)

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

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