def _get_yarr(self, endog):
        if data_util._is_structured_ndarray(endog):
            endog = data_util.struct_to_ndarray(endog)
        endog = np.asarray(endog)
        if len(endog) == 1:  # never squeeze to a scalar
            if endog.ndim == 1:
                return endog
            elif endog.ndim > 1:
                return np.asarray([endog.squeeze()])

        return endog.squeeze()

    def __init__(self):
        self.p = 2
        sdata, dates = get_lutkepohl_data('e1')

        data = data_util.struct_to_ndarray(sdata)
        adj_data = np.diff(np.log(data), axis=0)
        # est = VAR(adj_data, p=2, dates=dates[1:], names=names)

        self.model = VAR(adj_data[:-16], dates=dates[1:-16], freq='Q')
        self.res = self.model.fit(maxlags=self.p)
        self.irf = self.res.irf(10)
        self.lut = E1_Results()

    def setup_class(cls):
        cls.p = 2
        sdata, dates = get_lutkepohl_data('e1')

        data = data_util.struct_to_ndarray(sdata)
        adj_data = np.diff(np.log(data), axis=0)
        # est = VAR(adj_data, p=2, dates=dates[1:], names=names)

        cls.model = VAR(adj_data[:-16], dates=dates[1:-16], freq='BQ-MAR')
        cls.res = cls.model.fit(maxlags=cls.p)
        cls.irf = cls.res.irf(10)
        cls.lut = E1_Results()

    def __init__(self):
        self.p = 2

        if not have_pandas():
            return

        sdata, dates = get_lutkepohl_data("e1")

        names = sdata.dtype.names
        data = data_util.struct_to_ndarray(sdata)
        adj_data = np.diff(np.log(data), axis=0)
        # est = VAR(adj_data, p=2, dates=dates[1:], names=names)

        self.model = VAR(adj_data[:-16], dates=dates[1:-16], names=names, freq="Q")
        self.res = self.model.fit(maxlags=self.p)
        self.irf = self.res.irf(10)
        self.lut = E1_Results()

 def _get_xarr(self, exog):
     if data_util._is_structured_ndarray(exog):
         exog = data_util.struct_to_ndarray(exog)
     return np.asarray(exog)

 def _get_yarr(self, endog):
     if data_util._is_structured_ndarray(endog):
         endog = data_util.struct_to_ndarray(endog)
     return np.asarray(endog).squeeze()

def _acovs_to_acorrs(acovs):
    sd = np.sqrt(np.diag(acovs[0]))
    return acovs / np.outer(sd, sd)

if __name__ == '__main__':
    import statsmodels.api as sm
    from statsmodels.tsa.vector_ar.util import parse_lutkepohl_data
    import statsmodels.tools.data as data_util

    np.set_printoptions(linewidth=140, precision=5)

    sdata, dates = parse_lutkepohl_data('data/%s.dat' % 'e1')

    names = sdata.dtype.names
    data = data_util.struct_to_ndarray(sdata)
    adj_data = np.diff(np.log(data), axis=0)
    # est = VAR(adj_data, p=2, dates=dates[1:], names=names)
    model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)
    # model = VAR(adj_data[:-16], dates=dates[1:-16], names=names)

    est = model.fit(maxlags=2)
    irf = est.irf()

    y = est.y[-2:]
    """
    # irf.plot_irf()

    # i = 2; j = 1
    # cv = irf.cum_effect_cov(orth=True)
    # print np.sqrt(cv[:, j * 3 + i, j * 3 + i]) / 1e-2