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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

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