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')

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

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

Parameters:

endogarray_like

2-d endogenous response variable. The independent variable.

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

datesarray_like

must match number of rows of endog

freqstr, optional

The frequency of the time-series. A Pandas offset or ‘B’, ‘D’, ‘W’, ‘M’, ‘A’, or ‘Q’. This is optional if dates are given.

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

missingstr

Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.

Attributes:

endog_names

Names of endogenous variables

exog_names

The names of the exogenous variables.

Methods

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

check_order
check_rank

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.