4.8.1.2.6. statsmodels.tsa.api.SVAR

class statsmodels.tsa.api.SVAR(endog, svar_type, dates=None, freq=None, A=None, B=None, missing='none')[source]

Fit VAR and then estimate structural components of A and B, defined:

\[Ay_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + B\]

ar(epsilon_t)

Parameters:

endog : array-like

1-d endogenous response variable. The independent variable.

dates
: array-like

must match number of rows of endog

svar_type
: str

“A” - estimate structural parameters of A matrix, B assumed = I “B” - estimate structural parameters of B matrix, A assumed = I “AB” - estimate structural parameters indicated in both A and B matrix

A
: array-like

neqs x neqs with unknown parameters marked with ‘E’ for estimate

B
: array-like

neqs x neqs with unknown parameters marked with ‘E’ for estimate

__init__(endog, svar_type, dates=None, freq=None, A=None, B=None, missing='none')[source]

4.8.1.2.6.1. Methods

__init__(endog, svar_type[, dates, freq, A, ...])
check_order(J)
check_rank(J)
fit([A_guess, B_guess, maxlags, method, ic, ...]) Fit the SVAR model and solve for structural parameters
from_formula(formula, data[, subset]) Create a Model from a formula and dataframe.
hessian(AB_mask) Returns numerical hessian.
information(params) Fisher information matrix of model
initialize() Initialize (possibly re-initialize) a Model instance.
loglike(params) Loglikelihood for SVAR model
predict(params[, exog]) After a model has been fit predict returns the fitted values.
score(AB_mask) Return the gradient of the loglike at AB_mask.

4.8.1.2.6.2. Attributes

endog_names
exog_names