def fit(self, 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):
        lags = maxlags

        if ic is not None:
            selections = self.select_order(maxlags=maxlags, verbose=verbose)
            if ic not in selections:
                raise Exception("%s not recognized, must be among %s"
                                % (ic, sorted(selections)))
            lags = selections[ic]
            if verbose:
                print('Using %d based on %s criterion' %  (lags, ic))
        else:
            if lags is None:
                lags = 1

        self.nobs = len(self.endog) - lags

        # initialize starting parameters
        start_params = self._get_init_params(A_guess, B_guess)

        return self._estimate_svar(start_params, lags, trend=trend,
                                   solver=solver, override=override,
                                   maxiter=maxiter, maxfun=maxfun)
Fit the SVAR model and solve for structural parameters
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/vector_ar/svar_model.py/SVAR.fit