statsmodels.tsa.vector_ar.svar_model.SVAR¶

class statsmodels.tsa.vector_ar.svar_model.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:

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

Parameters:¶

endogarray_like

1-d endogenous response variable. The independent variable.

datesarray_like

must match number of rows of endog

svar_typestr

“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

Aarray_like

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

Barray_like

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

Attributes:¶

endog_names

Names of endogenous variables.

exog_names

The names of the exogenous variables.

y

References

Hamilton (1994) Time Series Analysis

Methods

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, drop_cols])	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.

Properties

endog_names	Names of endogenous variables.
exog_names	The names of the exogenous variables.
y