def _get_init_params(self, A_guess, B_guess):

        var_type = self.svar_type.lower()

        n_masked_a = self.A_mask.sum()
        if var_type in ['ab', 'a']:
            if A_guess is None:
                A_guess = np.array([.1]*n_masked_a)
            else:
                if len(A_guess) != n_masked_a:
                    msg = 'len(A_guess) = %s, there are %s parameters in A'
                    raise ValueError(msg % (len(A_guess), n_masked_a))
        else:
            A_guess = []

        n_masked_b = self.B_mask.sum()
        if var_type in ['ab', 'b']:
            if B_guess is None:
                B_guess = np.array([.1]*n_masked_b)
            else:
                if len(B_guess) != n_masked_b:
                    msg = 'len(B_guess) = %s, there are %s parameters in B'
                    raise ValueError(msg % (len(B_guess), n_masked_b))
        else:
            B_guess = []

        return np.r_[A_guess, B_guess]
Returns either the given starting or .1 if none are given.
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/vector_ar/svar_model.py/SVAR._get_init_params