statsmodels.tsa.vector_ar.svar_model.SVAR.fit

SVAR.fit(A_guess=None, B_guess=None, maxlags=None, method='ols', ic=None, trend='c', verbose=False, s_method='mle', solver='bfgs', override=False, maxiter=500, maxfun=500)

Fit the SVAR model and solve for structural parameters

Parameters:

A_guessarray_like, optional

A vector of starting values for all parameters to be estimated in A.

B_guessarray_like, optional

A vector of starting values for all parameters to be estimated in B.

maxlagsint

Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4), see select_order function

method{‘ols’}

Estimation method to use

ic{‘aic’, ‘fpe’, ‘hqic’, ‘bic’, None}

Information criterion to use for VAR order selection. aic : Akaike fpe : Final prediction error hqic : Hannan-Quinn bic : Bayesian a.k.a. Schwarz

verbosebool, default False

Print order selection output to the screen

trend, str {“c”, “ct”, “ctt”, “n”}

“c” - add constant “ct” - constant and trend “ctt” - constant, linear and quadratic trend “n” - co constant, no trend Note that these are prepended to the columns of the dataset.

s_method{‘mle’}

Estimation method for structural parameters

solver{‘nm’, ‘newton’, ‘bfgs’, ‘cg’, ‘ncg’, ‘powell’}

Solution method See statsmodels.base for details

overridebool, default False

If True, returns estimates of A and B without checking order or rank condition

maxiterint, default 500

Number of iterations to perform in solution method

maxfunint

Number of function evaluations to perform

Returns:

estSVARResults

Notes

Lütkepohl pp. 146-153 Hamilton pp. 324-336