Statsmodel SVAR (python) - unable to add exogenous variables?

Question

I hope you can help me with an issue I am having with the python time series code for SVAR from statsmodels.tsa.vector_ar.svar_model.

When I run the SVAR command below it appears that I am unable to include exogenous variables in the model. In the standard VAR code you are able to add exogenous variables to your model, and then run impulse responses.

The code for SVAR is as follows:

A = np.asarray([[1,0,0,0,0],['E',1,0,0,0], ['E','E',1,0,0], ['E','E','E',1,0],['E','E','E','E',1]])
B = np.asarray([['E',0,0,0,0],[0,'E',0,0,0], [0,0,'E',0,0], [0,0,0,'E',0],[0,0,0,0,'E']])

endogenous_vars = ["r","dyca", "infl", "dm","dex"]
exogenous_vars=["dpcm"]

model = SVAR(reg_data[endogenous_vars], svar_type='AB', A=A, B=B)
results = model.fit(maxlags=12, solver='bfgs')

irf=results.irf(periods=48)
irf.plot()

response=['dyca','dm','dex']  #<--- these are the first-differenced variables with a trend, so I use impulse accumulated effects to get the correct output graphs

# for reasons I am not sure, I can not get the three vars on the same graph so I run a loop to generate a graph for each one

for i in response:
    irf.plot_cum_effects(impulse='r', response=i, plot_stderr=True, stderr_type='mc', repl=1500)

But every time I run this code, I get the same error:

irf.plot_cum_effects(impulse='r', response=i, plot_stderr=True, stderr_type='mc', repl=1500)
stderr = self.cum_errband_mc(orth=orth, repl=repl,
return model.irf_errband_mc(orth=orth, repl=repl,
ma_coll = self.irf_resim(
ma_coll[i, :, :, :] = fill_coll(sim)

ret = VAR(sim, exog=self.exog).fit(maxlags=k_ar, trend=self.trend)

SVARResults' object has no attribute 'exog'

self.exog is defined only in the source code:

_model_type = 'SVAR'

    def __init__(self, endog, endog_lagged, params, sigma_u, lag_order,
                 A=None, B=None, A_mask=None, B_mask=None, model=None,
                 trend='c', names=None, dates=None):

        self.model = model
        self.endog = endog
        self.endog_lagged = endog_lagged
        self.dates = dates

        self.n_totobs, self.neqs = self.endog.shape
        self.nobs = self.n_totobs - lag_order
        k_trend = util.get_trendorder(trend)
        if k_trend > 0: # make this the polynomial trend order
            trendorder = k_trend - 1
        else:
            trendorder = None
        self.k_trend = k_trend
        self.k_exog = k_trend  # now (0.9) required by VARProcess
        self.trendorder = trendorder

        self.exog_names = util.make_lag_names(names, lag_order, k_trend)
        self.params = params
        self.sigma_u = sigma_u

        # Each matrix needs to be transposed
        reshaped = self.params[self.k_trend:]
        reshaped = reshaped.reshape((lag_order, self.neqs, self.neqs))

        # Need to transpose each coefficient matrix
        intercept = self.params[0]
        coefs = reshaped.swapaxes(1, 2).copy()

        #SVAR components
        #TODO: if you define these here, you do not also have to define
        #them in SVAR process, but I left them for now -ss
        self.A = A
        self.B = B
        self.A_mask = A_mask
        self.B_mask = B_mask

        super().__init__(coefs, intercept, sigma_u, A, B,
                                          names=names)

The issue as I can see it, is that in the SVAR code line, I am unable to specify any exogenous variables.

Am I missing something really simple? Any and all help would be greatly appreciated.