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)[source]¶ 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.
- maxlags
int 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,
defaultFalse Print order selection output to the screen
- trend, str {“c”, “ct”, “ctt”, “nc”}
“c” - add constant “ct” - constant and trend “ctt” - constant, linear and quadratic trend “nc” - 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,
defaultFalse If True, returns estimates of A and B without checking order or rank condition
- maxiter
int,default500 Number of iterations to perform in solution method
- maxfun
int Number of function evaluations to perform
- A_guessarray_like,
- Returns
- est
SVARResults
- est
Notes
Lütkepohl pp. 146-153 Hamilton pp. 324-336